data mining research topics for phd

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Artificial Intelligence and Machine Learning and Data Mining

Computer scientists introduce innovative new work at annual conferences. The Artificial Intelligence and Machine Learning and Data Mining research community expands the state of the art at these, the field's most prestigious and selective conferences:

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Artificial Intelligence (AI) researchers now predict that computers will be able to perform tasks that were once considered the prerogative of human beings.

They include tasks such as driving trucks, translating languages, writing high school essays, creating art, analyzing forensic evidence, and even work as a surgeon. Although some of these goals are predicted to happen over several decades, AI is concerned with principles and algorithms that allow researchers to make such bold predictions. Current methods focus on variants of deep learning — such as convolutional nets, recurrent nets, autoencoders and adversarial networks — as well as on the methods of probabilistic graphical models.

School/University Centers and Institutes

Center for Unified Biometrics and Sensors (CUBS)
Center of Excellence for Document Analysis and Recognition (CEDAR)
UB Artificial Intelligence Institute (AII)
UB Center for Cognitive Science (CCS)

CSE Research Labs and Groups

Artificial Intelligence Innovation Lab (A2IL)
UB Data Science Research Group
UB Media Forensic Lab
Visual Computing Lab
X-Lab@UB: Accelerating AI Systems & Solutions

Affiliated Faculty

113I Davis Hall

Phone: (716) 645-1590

[email protected]

Research Topics: Wireless systems; mobile computing; Internet of Things (IoT); wireless sensing; machine learning

213 Capen Hall

Phone: (716) 645-4747

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Research Topics: Big data analytics; anomaly detection

338L Davis Hall

Phone: (716) 645-4750

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Research Topics: Large-scale Bayesian sampling and inference; deep generative models such as VAE and GAN; deep reinforcement learning with Bayesian methods

349 Davis Hall

Phone: (716) 645-4769

[email protected]

Research Topics: Computer vision; machine learning; multimodal data analytics; pattern recognition; large-scale visual search and mining; big data analytics

338P Davis Hall

Phone: Department Chair: (716) 645-4730, Faculty Office: (716) 645-1557

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Research Topics: Document image understanding; video analysis; pattern recognition; computer vision; media forensics; artificial intelligence

347 Davis Hall

Phone: (716) 645-2834

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Research Topics: Big healthcare data; medical imaging informatics; computer vision; machine learning

516 Capen Hall, 113 Davis Hall

Phone: (716) 645-3321, (716) 645-1558

[email protected]

Research Topics: Pattern recognition; digital libraries; biometrics

338G Davis Hall

Phone: (716) 645-0306

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Research Topics: Optimization algorithms; machine learning; big data analytics; federated learning and networks

338J Davis Hall

Phone: (716) 645-2323

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Research Topics: Data clouds; data-intensive computing; petascale distributed systems; storage and I/O optimization

332 Davis Hall

Phone: (716) 645-4754

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Research Topics: Optimization; deep learning; foundation models; computer vision and machine learning

317 Davis Hall

Phone: (716) 645-1587

[email protected]

Research Topics: digital media forensics; computer vision; machine learning

305 Davis Hall

Phone: (716) 645-1588

[email protected]

Research Topics: Human behavior modeling; sign language understanding; probabilistic modeling

351 Davis Hall

Phone: (716) 645-4757

[email protected]

Research Topics: Computer science education; wireless communications; wireless sensor network; routing protocol; cognitive radio network; software-defined radio; machine learning

113K Davis Hall

Phone: (716) 645-1564

[email protected]

Research Topics: Computer vision; artificial intelligence; biometrics and fairness; and trust in AI

326 Davis Hall

Phone: (716) 645-4738

[email protected]

Research Topics: Mathematical logic; theoretical computer science

319 Davis Hall

Phone: (716) 645-2464

[email protected]

Research Topics: Structured linear algebra; society and computing; coding theory; database algorithms

323 Davis Hall

Phone: (716) 645-1592

[email protected]

Research Topics: Graph mining; social network analysis; network science; temporal network analysis; combinatorial scientific computing; stream processing; distributed and parallel computing

338C Davis Hall

Phone: (716) 645-1602

[email protected]

Research Topics: Information extraction; information retrieval; multimedia information retrieval; text mining

350 Davis Hall

Phone: (716) 645-1586

[email protected]

Research Topics: Optimal control in complex systems, including social behavior modeling, deep reinforcement learning, multi-agent settings, deep learning, adversarial machine learning, transportation and large-scale social system dynamics

316 Davis Hall

Phone: (716) 645-4760

[email protected]

Research Topics: Cognitive computing, big data analytics, deep learning, smarter energy, application of cognitive computing for industrial solutions

315 Davis Hall

Phone: (716) 645-4734

[email protected]

Research Topics: Algorithms; computational geometry; machine learning; differential privacy; geometric computing in deep learning and biomedical applications

338H Davis Hall

Phone: (716) 645-0562

[email protected]

Research Topics: Computer vision; pattern recognition; video analytics; large-scale visual search and mining

Research Ranking

UB's institutional reputation in the field of computer science has improved dramatically over the last decade. By the most valid measure, our national ranking has risen from 50th to 29th .

The Computing Research Association (CRA) is a leading computer science advocacy organization whose mission is to unite industry, academia, and government. The CRA recommends CSRankings: Computer Science Rankings as the best institutional ranking agency, preferring it over the traditional standard, the US News and World Report Best Graduate Schools report.

The CRA supports the CSRankings report because its evaluative criteria meet the ' GOTO ' standard:

Good data . Data have been cleaned and curated.

Open . Data available, regarding attributes measured, at least for verification.

Transparent . Process and methodologies are entirely transparent.

Objective . Based on measurable attributes.

For more details, see Department Rankings , by H.V. Jagadish .

According to CSRankings (2008-2018) , UB's 10-year computer science institutional ranking is #50 in the nation, tied with the University of Central Florida and the University of North Carolina .

According to CSRankings (2015-2018) , UB's three-year computer science institutional ranking is #34 in the nation, making our peer institution the University of Virginia .

According to CSRankings (2017-2018) , UB's one-year computer science institutional ranking is #29 in the nation, putting us in company with Harvard , Johns Hopkins , Ohio State , and Penn State .

Research Highlights

Wenyao Xu leads an NSF-funded program that detects 3D printing data security vulnerabilities by using smart phones to analyze electromagnetic and acoustic waves. Kui Ren and Chi Zhou are co-authors.

Karthik Dantu owns the vision component of the RoboBee Initiative , led by the National Science Foundation and Harvard University. The "eyes" that Dr. Dantu is integrating are laser-powered sensors that enable the mechanical bees to orient themselves in space.

An article on PhysOrg reports UB has received a $584,469 grant from the National Science Foundation to create a tool designed to work with the existing computing infrastructure to boost data transfer speeds by more than 10 times, and quotes Tevfik Kosar , associate professor of computer science.

Wenyao Xu created AutoDietary — software that tracks the unique sounds produced by food as people chew it. AutoDietary, placed near the throat by a necklace delivery system developed at China's Northeastern University, helps users measure their caloric intake.

Ken Regan develops algorithms that detect cheating in chess games. His software compares a player's moves to a database of the player's typical gameplay, then makes an assessment of the statistical likelihood of cheating. Dr. Regan frequently consults at international chess matches.

Professor and Chair Chunming Qiao leads Instrument for Connected and Autonomous Vehicle Evaluation and Experimentation (iCAVE2) —a multidisciplinary academic-industrial partnership that's helping to make self-driving cars safer, cleaner, and more efficient.

Proposed software solution could extend battery life, reduce energy consumption.

Recognitions

The three SEAS faculty members have been named recipients of the 2024 SUNY Chancellor’s Award for Excellence in Scholarship and Creative Activities

Multiple faculty members and students from SEAS were nominated by students, faculty and staff for Pillar of Leadership Awards.

Deborah Chung and Venu Govindaraju will receive the UB President’s Medal, recognizing extraordinary service to the university.

Jinjun Xiong, SUNY Empire Innovation Professor in the Department of Computer Science and Engineering, has been elevated to fellow in the Institute of Electrical and Electronics Engineers.

Awards acknowledge and provide system-wide recognition for consistently superior professional achievement and the ongoing pursuit of excellence.

Our Promise
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PhD Research Topics in Data Mining

In recent times, there is a massive growth in information generation through “IoT.” At the same time, it stores in “Cloud Computing.” PhD Research Topics in Data Mining is the academic stock of hot topics. It intends to convert our line of thoughts to your research As a result, it ‘ opens the way for research in data mining.’ Hence, join us to put your career on the right track of data mining. So that you will get ‘thrice times better success in your PhD.’

SOUNDFUL TOPICS

DNA and also quantum computing for data mining
Spatial data mining
Graph theory for information retrieval
Semantic web mining
Multimedia retrieval
Personalized recommender systems
Data warehousing integration
Mining from low-quality sources
Database management for information storage
Context-aware computing and also in content-based retrieval
Low-quality audio mining
Multimedia quality assessment
Social network sentiment analysis
P2P and grid databases management
Data mining for IoT applications
MapReduce optimization for itemset mining

Our tireless pros from PhD Research Topics in Data Mining will uplift your research through their energetic ideas. On the whole, we are here to polish each nook of your research . For this reason, we also work on apt selection of simulation tools, datasets, and journals .

DATASETS FOR IDS

ISCXIDS2012

PhD Research Topics in data Mining

Be Smart and Go With Our PhD Research Topics in Data Mining On the road to Huge Success!!!

Analysis of Large-Scale Spatio-Temporal Data using Progressive Partition and Multidimensional Pattern Extraction

Recursive Event Sequence Exploration using Interweaving Queries and Pattern Mining

An Effective Minimum Spanning Tree Clustering for Anti-Noise Process Mining Algorithm

Visual Analytics of Scientific Data Sets using Graph-Based Techniques

An Analysis of Data Flow and Visualization for Spatiotemporal Statistical Data without Trajectory Information

Multimodal Data Correlation for Device Clustering Algorithm in Cognitive Internet of Things

Improved STRAP –Based Dynamic Clustering Scheme for Evolving Data Streams

Distributed storage system for electric power data using Hbase

Itemset Mining Methods for Detection of Frequent Alarm Patterns in Industrial Alarm Floods

An Efficient Algorithm for Clustering Categorical Data With Set-Valued Features

A Privacy Preserving in Multi-Access Edge Computing for Heterogeneous IoT over Big Data

Hidden Temporal Information and Rule-Based Entity Resolution on Database

A Automatic Fault Diagnosis and Prognosis for Distribution Automation using Data Analytic Methodology

Leveraging Graph Mining based on Compression for Behavior-Based Malware Detection

An Efficient IoT Enabled Parallel Mining Algorithm Representative Pattern Set of Large-Scale Itemsets

Cluster-Aided Wireless Channel Modeling based on Big Data Algorithms

IoT Enabled Three Hierarchical Levels of Big-Data Market Model in Multiple Data Sources

A Methodology to discovering companion patterns using traffic data stream

A Clustering based on Uncertain Data in Distributed Peer-to-Peer Networks

Grammar-Based Genetic Programming for Mining Context-Aware Association Rules

MILESTONE 1: Research Proposal

Finalize journal (indexing).

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We helping you with right and perfect topic selection, which sound interesting to the other fellows of your committee. For e.g. if your interest in networking, the research topic is VANET / MANET / any other

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To ensure the novelty of research, we find research gaps in 50+ latest benchmark papers (IEEE, Springer, Elsevier, MDPI, Hindawi, etc.)

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After literature survey, we get the main issue/problem that your research topic will aim to resolve and elegant writing support to identify relevance of the issue.

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Writing a good research proposal has need of lot of time. We only span a few to cover all major aspects (reference papers collection, deficiency finding, drawing system architecture, highlights novelty)

MILESTONE 2: System Development

Fix implementation plan.

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Pseudocode Description

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Comparison/Experiments

We perform the comparison between proposed and existing schemes in both quantitative and qualitative manner since it is most crucial part of any journal paper.

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For every project order, we deliver the following: reference papers, source codes screenshots, project video, installation and running procedures.

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Choosing right format.

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Before paper writing, we collect reliable resources such as 50+ journal papers, magazines, news, encyclopedia (books), benchmark datasets, and online resources.

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MILESTONE 5: Thesis Writing

Identifying university format.

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Research Topics & Ideas: Data Science

50 Topic Ideas To Kickstart Your Research Project

Research topics and ideas about data science and big data analytics

If you’re just starting out exploring data science-related topics for your dissertation, thesis or research project, you’ve come to the right place. In this post, we’ll help kickstart your research by providing a hearty list of data science and analytics-related research ideas , including examples from recent studies.

PS – This is just the start…

We know it’s exciting to run through a list of research topics, but please keep in mind that this list is just a starting point . These topic ideas provided here are intentionally broad and generic , so keep in mind that you will need to develop them further. Nevertheless, they should inspire some ideas for your project.

To develop a suitable research topic, you’ll need to identify a clear and convincing research gap , and a viable plan to fill that gap. If this sounds foreign to you, check out our free research topic webinar that explores how to find and refine a high-quality research topic, from scratch. Alternatively, consider our 1-on-1 coaching service .

Data Science-Related Research Topics

Developing machine learning models for real-time fraud detection in online transactions.
The use of big data analytics in predicting and managing urban traffic flow.
Investigating the effectiveness of data mining techniques in identifying early signs of mental health issues from social media usage.
The application of predictive analytics in personalizing cancer treatment plans.
Analyzing consumer behavior through big data to enhance retail marketing strategies.
The role of data science in optimizing renewable energy generation from wind farms.
Developing natural language processing algorithms for real-time news aggregation and summarization.
The application of big data in monitoring and predicting epidemic outbreaks.
Investigating the use of machine learning in automating credit scoring for microfinance.
The role of data analytics in improving patient care in telemedicine.
Developing AI-driven models for predictive maintenance in the manufacturing industry.
The use of big data analytics in enhancing cybersecurity threat intelligence.
Investigating the impact of sentiment analysis on brand reputation management.
The application of data science in optimizing logistics and supply chain operations.
Developing deep learning techniques for image recognition in medical diagnostics.
The role of big data in analyzing climate change impacts on agricultural productivity.
Investigating the use of data analytics in optimizing energy consumption in smart buildings.
The application of machine learning in detecting plagiarism in academic works.
Analyzing social media data for trends in political opinion and electoral predictions.
The role of big data in enhancing sports performance analytics.
Developing data-driven strategies for effective water resource management.
The use of big data in improving customer experience in the banking sector.
Investigating the application of data science in fraud detection in insurance claims.
The role of predictive analytics in financial market risk assessment.
Developing AI models for early detection of network vulnerabilities.

Data Science Research Ideas (Continued)

The application of big data in public transportation systems for route optimization.
Investigating the impact of big data analytics on e-commerce recommendation systems.
The use of data mining techniques in understanding consumer preferences in the entertainment industry.
Developing predictive models for real estate pricing and market trends.
The role of big data in tracking and managing environmental pollution.
Investigating the use of data analytics in improving airline operational efficiency.
The application of machine learning in optimizing pharmaceutical drug discovery.
Analyzing online customer reviews to inform product development in the tech industry.
The role of data science in crime prediction and prevention strategies.
Developing models for analyzing financial time series data for investment strategies.
The use of big data in assessing the impact of educational policies on student performance.
Investigating the effectiveness of data visualization techniques in business reporting.
The application of data analytics in human resource management and talent acquisition.
Developing algorithms for anomaly detection in network traffic data.
The role of machine learning in enhancing personalized online learning experiences.
Investigating the use of big data in urban planning and smart city development.
The application of predictive analytics in weather forecasting and disaster management.
Analyzing consumer data to drive innovations in the automotive industry.
The role of data science in optimizing content delivery networks for streaming services.
Developing machine learning models for automated text classification in legal documents.
The use of big data in tracking global supply chain disruptions.
Investigating the application of data analytics in personalized nutrition and fitness.
The role of big data in enhancing the accuracy of geological surveying for natural resource exploration.
Developing predictive models for customer churn in the telecommunications industry.
The application of data science in optimizing advertisement placement and reach.

Recent Data Science-Related Studies

While the ideas we’ve presented above are a decent starting point for finding a research topic, they are fairly generic and non-specific. So, it helps to look at actual studies in the data science and analytics space to see how this all comes together in practice.

Below, we’ve included a selection of recent studies to help refine your thinking. These are actual studies, so they can provide some useful insight as to what a research topic looks like in practice.

Data Science in Healthcare: COVID-19 and Beyond (Hulsen, 2022)
Auto-ML Web-application for Automated Machine Learning Algorithm Training and evaluation (Mukherjee & Rao, 2022)
Survey on Statistics and ML in Data Science and Effect in Businesses (Reddy et al., 2022)
Visualization in Data Science VDS @ KDD 2022 (Plant et al., 2022)
An Essay on How Data Science Can Strengthen Business (Santos, 2023)
A Deep study of Data science related problems, application and machine learning algorithms utilized in Data science (Ranjani et al., 2022)
You Teach WHAT in Your Data Science Course?!? (Posner & Kerby-Helm, 2022)
Statistical Analysis for the Traffic Police Activity: Nashville, Tennessee, USA (Tufail & Gul, 2022)
Data Management and Visual Information Processing in Financial Organization using Machine Learning (Balamurugan et al., 2022)
A Proposal of an Interactive Web Application Tool QuickViz: To Automate Exploratory Data Analysis (Pitroda, 2022)
Applications of Data Science in Respective Engineering Domains (Rasool & Chaudhary, 2022)
Jupyter Notebooks for Introducing Data Science to Novice Users (Fruchart et al., 2022)
Towards a Systematic Review of Data Science Programs: Themes, Courses, and Ethics (Nellore & Zimmer, 2022)
Application of data science and bioinformatics in healthcare technologies (Veeranki & Varshney, 2022)
TAPS Responsibility Matrix: A tool for responsible data science by design (Urovi et al., 2023)
Data Detectives: A Data Science Program for Middle Grade Learners (Thompson & Irgens, 2022)
MACHINE LEARNING FOR NON-MAJORS: A WHITE BOX APPROACH (Mike & Hazzan, 2022)
COMPONENTS OF DATA SCIENCE AND ITS APPLICATIONS (Paul et al., 2022)
Analysis on the Application of Data Science in Business Analytics (Wang, 2022)

As you can see, these research topics are a lot more focused than the generic topic ideas we presented earlier. So, for you to develop a high-quality research topic, you’ll need to get specific and laser-focused on a specific context with specific variables of interest. In the video below, we explore some other important things you’ll need to consider when crafting your research topic.

Get 1-On-1 Help

If you’re still unsure about how to find a quality research topic, check out our Research Topic Kickstarter service, which is the perfect starting point for developing a unique, well-justified research topic.

Research Topic Kickstarter - Need Help Finding A Research Topic?

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Distance Based Pattern Driven Mining for Outlier Detection in High Dimensional Big Dataset

Detection of outliers or anomalies is one of the vital issues in pattern-driven data mining. Outlier detection detects the inconsistent behavior of individual objects. It is an important sector in the data mining field with several different applications such as detecting credit card fraud, hacking discovery and discovering criminal activities. It is necessary to develop tools used to uncover the critical information established in the extensive data. This paper investigated a novel method for detecting cluster outliers in a multidimensional dataset, capable of identifying the clusters and outliers for datasets containing noise. The proposed method can detect the groups and outliers left by the clustering process, like instant irregular sets of clusters (C) and outliers (O), to boost the results. The results obtained after applying the algorithm to the dataset improved in terms of several parameters. For the comparative analysis, the accurate average value and the recall value parameters are computed. The accurate average value is 74.05% of the existing COID algorithm, and our proposed algorithm has 77.21%. The average recall value is 81.19% and 89.51% of the existing and proposed algorithm, which shows that the proposed work efficiency is better than the existing COID algorithm.

Implementation of Data Mining Technology in Bonded Warehouse Inbound and Outbound Goods Trade

For the taxed goods, the actual freight is generally determined by multiplying the allocated freight for each KG and actual outgoing weight based on the outgoing order number on the outgoing bill. Considering the conventional logistics is insufficient to cope with the rapid response of e-commerce orders to logistics requirements, this work discussed the implementation of data mining technology in bonded warehouse inbound and outbound goods trade. Specifically, a bonded warehouse decision-making system with data warehouse, conceptual model, online analytical processing system, human-computer interaction module and WEB data sharing platform was developed. The statistical query module can be used to perform statistics and queries on warehousing operations. After the optimization of the whole warehousing business process, it only takes 19.1 hours to get the actual freight, which is nearly one third less than the time before optimization. This study could create a better environment for the development of China's processing trade.

Multi-objective economic load dispatch method based on data mining technology for large coal-fired power plants

User activity classification and domain-wise ranking through social interactions.

Twitter has gained a significant prevalence among the users across the numerous domains, in the majority of the countries, and among different age groups. It servers a real-time micro-blogging service for communication and opinion sharing. Twitter is sharing its data for research and study purposes by exposing open APIs that make it the most suitable source of data for social media analytics. Applying data mining and machine learning techniques on tweets is gaining more and more interest. The most prominent enigma in social media analytics is to automatically identify and rank influencers. This research is aimed to detect the user's topics of interest in social media and rank them based on specific topics, domains, etc. Few hybrid parameters are also distinguished in this research based on the post's content, post’s metadata, user’s profile, and user's network feature to capture different aspects of being influential and used in the ranking algorithm. Results concluded that the proposed approach is well effective in both the classification and ranking of individuals in a cluster.

A data mining analysis of COVID-19 cases in states of United States of America

Epidemic diseases can be extremely dangerous with its hazarding influences. They may have negative effects on economies, businesses, environment, humans, and workforce. In this paper, some of the factors that are interrelated with COVID-19 pandemic have been examined using data mining methodologies and approaches. As a result of the analysis some rules and insights have been discovered and performances of the data mining algorithms have been evaluated. According to the analysis results, JRip algorithmic technique had the most correct classification rate and the lowest root mean squared error (RMSE). Considering classification rate and RMSE measure, JRip can be considered as an effective method in understanding factors that are related with corona virus caused deaths.

Exploring distributed energy generation for sustainable development: A data mining approach

A comprehensive guideline for bengali sentiment annotation.

Sentiment Analysis (SA) is a Natural Language Processing (NLP) and an Information Extraction (IE) task that primarily aims to obtain the writer’s feelings expressed in positive or negative by analyzing a large number of documents. SA is also widely studied in the fields of data mining, web mining, text mining, and information retrieval. The fundamental task in sentiment analysis is to classify the polarity of a given content as Positive, Negative, or Neutral . Although extensive research has been conducted in this area of computational linguistics, most of the research work has been carried out in the context of English language. However, Bengali sentiment expression has varying degree of sentiment labels, which can be plausibly distinct from English language. Therefore, sentiment assessment of Bengali language is undeniably important to be developed and executed properly. In sentiment analysis, the prediction potential of an automatic modeling is completely dependent on the quality of dataset annotation. Bengali sentiment annotation is a challenging task due to diversified structures (syntax) of the language and its different degrees of innate sentiments (i.e., weakly and strongly positive/negative sentiments). Thus, in this article, we propose a novel and precise guideline for the researchers, linguistic experts, and referees to annotate Bengali sentences immaculately with a view to building effective datasets for automatic sentiment prediction efficiently.

Capturing Dynamics of Information Diffusion in SNS: A Survey of Methodology and Techniques

Studying information diffusion in SNS (Social Networks Service) has remarkable significance in both academia and industry. Theoretically, it boosts the development of other subjects such as statistics, sociology, and data mining. Practically, diffusion modeling provides fundamental support for many downstream applications (e.g., public opinion monitoring, rumor source identification, and viral marketing). Tremendous efforts have been devoted to this area to understand and quantify information diffusion dynamics. This survey investigates and summarizes the emerging distinguished works in diffusion modeling. We first put forward a unified information diffusion concept in terms of three components: information, user decision, and social vectors, followed by a detailed introduction of the methodologies for diffusion modeling. And then, a new taxonomy adopting hybrid philosophy (i.e., granularity and techniques) is proposed, and we made a series of comparative studies on elementary diffusion models under our taxonomy from the aspects of assumptions, methods, and pros and cons. We further summarized representative diffusion modeling in special scenarios and significant downstream tasks based on these elementary models. Finally, open issues in this field following the methodology of diffusion modeling are discussed.

The Influence of E-book Teaching on the Motivation and Effectiveness of Learning Law by Using Data Mining Analysis

This paper studies the motivation of learning law, compares the teaching effectiveness of two different teaching methods, e-book teaching and traditional teaching, and analyses the influence of e-book teaching on the effectiveness of law by using big data analysis. From the perspective of law student psychology, e-book teaching can attract students' attention, stimulate students' interest in learning, deepen knowledge impression while learning, expand knowledge, and ultimately improve the performance of practical assessment. With a small sample size, there may be some deficiencies in the research results' representativeness. To stimulate the learning motivation of law as well as some other theoretical disciplines in colleges and universities has particular referential significance and provides ideas for the reform of teaching mode at colleges and universities. This paper uses a decision tree algorithm in data mining for the analysis and finds out the influencing factors of law students' learning motivation and effectiveness in the learning process from students' perspective.

Intelligent Data Mining based Method for Efficient English Teaching and Cultural Analysis

The emergence of online education helps improving the traditional English teaching quality greatly. However, it only moves the teaching process from offline to online, which does not really change the essence of traditional English teaching. In this work, we mainly study an intelligent English teaching method to further improve the quality of English teaching. Specifically, the random forest is firstly used to analyze and excavate the grammatical and syntactic features of the English text. Then, the decision tree based method is proposed to make a prediction about the English text in terms of its grammar or syntax issues. The evaluation results indicate that the proposed method can effectively improve the accuracy of English grammar or syntax recognition.

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PhD Projects in Data Mining

PhD Projects in Data Mining is ready to invent new research work that will uplift your career. We offer a hi-tech set up for PhD pupils who want to do a project in data mining. In many ways, Data Mining stands as an active research area also with plenty of uses.

‘Data Mining will involve gathering data and also finding any pattern present in there.’ Additionally, it will also aid in the dealing out of that data into useful info. Often it will imply other areas such as IoT, cloud computing, big data, and so on. Our experts will also carry out a thorough data mining project analysis.

Buy Research PhD Projects in Data Mining Online

DATASET FOR DATA MINING PROJECTS

Uci machine learning repository.

Hepatitis C Virus (HCV) for Egyptian Patients
Human Activity Recognition also from Continuous Ambient Sensor Data
Beijing Multi-Site Air-Quality Data
WISDM Smartphone and Smartwatch Activity and also in Biometrics Dataset

Insider & Intrusion Threats Dataset

KDD Cup 99 dataset
NSL KDD Dataset
CIDDS Dataset
ADFA-IDS 2017
UGR Dataset
CIC IDS Dataset
Contagio-CTU-UNB
ADFA Intrusion Detection Datasets
And also in University of Newbrunswick datasets

PhD Projects in Data Mining will provide the Neophytes’ technical platform to pursue their research in a realistic manner. We will also respect any of your data mining project ideas and assure to give the utmost care.

Most Researched Data Mining Topics in Current Days

Graph Mining for Malware Detection
Data Assimilation by Neural Networks
Task-Oriented Pattern Mining
Big Data Mining
Cyber Security for Massive Data
5G Technology
Software Defined Networking
Information Security
Distributed Data Mining
Blockchain also in Data Analytics
Cluster Analysis for Data Mining
Mining with Deep Learning

You can get the Data Mining projects code alone from our experts. All you need to do is, come and also explain your concept with input and output. Our experts will start your code in the language and tool that you stated. Without delay, you can get your code on time.

PhD Projects in Data Mining will help you heed your failures and move ahead to succeed. We also hold an incisive crew of 150+ top rate experts to aid you in any tool.

PROMINENT DATA MINING TOOLS

IBM SPSS Modeler
And also in Hadoop

At this point, we will finish all your project work and wrap it after corrections. Next, you will also get a project with all the add-ons. Our experts will explain all the terms in your work to clarify your doubts. Perhaps, you need the details one more time. Then, just make a call to our help desk, and we will be at your service.

Without a plan, your research is idle; blend with us to take your research to the next level!!!’

In the final analysis, go through the few newfangled ideas in Data Mining,

Uncertain Sensor Data for Trajectory Mining

Mining High-Utility Itemsets using Selective Database Projections Based Methodology

Mining Frequent Patterns using MapReduce-Based Apriori Versions on Big Data

A Real-Time Massive Data Processing Method for Densely Distributed Sensor Networks

A Novel Association Rule Mining Approach for Probabilistic Graph Model –Based Power Transformers State Parameters in big Data

A Big Data Analytics Oriented Data Engineering based on Schema Theory in Gene Expression Programming

Prediction of Hospital Admissions From the Emergency Department in Data Mining

A Method of Mining Hidden Transition of Business Process using Region

An Efficient Novel Upper-Bounds-Based Vertical Mining of High Average-Utility Itemsets

Data mining complex correlations for Islanding detection of synchronous distributed generators

An Algorithm of Weighted Frequent Itemset Mining for Intelligent Decision in Smart Systems

An Alternative Method: Estimating 3-D Large Displacements of Mining Areas from a Single SAR Amplitude Pair based on Offset Tracking

A Chronic disease progression mining using Heterogeneous network

Personalized E-Learning Model - Integration of Data Mining Clustering Techniques

Analyze Travel Time in Road-Based Mass Transit Systems using Systematic Approach in Data Mining

Privacy preserving: association rule hiding based on fuzzy logic approach for big data mining

Reducing Redundancy for Prevalent Co-Location Patterns

A Goal-oriented Requirement Analysis Method for Non-Expert Users - Data Mining Techniques Selection

Line Trip Fault Prediction using Data in Power Systems based on LSTM Networks and SVM

A Real-Time PCA –Based Applications using Indirect Power-System Contingency Screening

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Phd Research Topics In Text Mining

Phd Research Topics In Web Mining

Phd Research Topics In Image Mining

Phd Research Topics In Opnet

Phd Research Topics In Web Technology

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Phd Research Topics In Routing

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Data Mining Dissertation Topics

The term “data mining” refers to an intelligent data lookup capacity that uses statistics-based algorithms and methodologies to find trends, patterns, links, and correlations within the collected data and records. Audio, Pictorial, Video, textual, online, and social media-based mining are only a few examples of data mining. This article will provide you with a complete overview of various recent data mining dissertation topics . Let us first start with the definition of data mining processes.

What is the data mining process?

The practice of evaluating a huge batch containing data to find different patterns is known as data mining.
Companies can utilize data mining for a variety of purposes, including knowing as to what consumers are engaged in or would like to buy, as well as detection of fraudulent activities and malware scanning.

Hence data mining plays a very significant role in both commercial and personal life aspects of the modern world. We have been working on data mining dissertation topics and project ideas for more than 15 years as a result of which we have gained huge expertise and have acquired vast knowledge, skills, and experience in the field. So we can guide you in all the existing and normal data mining methods and techniques. Let us now talk about the data mining techniques below

Data mining techniques

Neural networks
Rule induction
Nearest neighbor classification
Decision tree
Descriptive techniques – sequential analysis, association, and clustering

Complete explanation and description on all these techniques and methods are available at our website on data mining dissertation topics . By understanding the importance of data mining, we have successfully worked out several advanced projects and implementations in real-time . Check out our website for all details about our successful projects in data mining. Let us now see about the data mining approaches below

Approaches in data mining

Belief nets
Neural nets (Kohonen and backpropagation)
Decision trees (CHAID, CAITT, and C 4.5)
Rules (genetic algorithms and induction)
Case-based reasoning
Nearest neighbor

This is the basic classification of the various data mining approaches that are in use today. With the support of the best engineers and world-class certified experts in data mining , we are here to provide you with a massive amount of reliable and authentic research data along with complete support in interpretation, analysis, and understanding them . Get in touch with us at any time for complete support for your data mining dissertation . We assure to give you full support and ultimate guidance on any data mining dissertation topics. We will now talk about the major issues in data mining

Major issues in data mining

Parallel, distributed, and incremental mining algorithms
Data mining algorithm efficiency and scalability
Incorporation of background data
Interactive meaning
Data mining result presentation and visualization
Pattern evaluation meaning
pattern and Constraint guided mining
Power boosting in networking environment
Data mining interdisciplinary approach
Data insufficiency and uncertainty
Handling the issues of noise
Multidimensional data mining space
Novel approaches and incorporating multiple aspects of data mining

We have handled all these issues efficiently and have devised successful methods to overcome them. Get in touch with us to know more about the potential data mining solutions and advanced techniques used in overcoming the issues of data mining . What are the top data mining topics?

Data Mining Tools

WEKA, Orange, Tanagra and NLTK
Angoss, Oracle, and STATISTICA (or StatSoft)
Pentaho, Rattle, and Apache Mahout
RapidMiner, R – programming, and KNIME
JHepWork, IBM SPSS, and SAS Enterprise Miner

The tips and advice in using these tools of data mining are explained in detail on our website. Also, we are here to help you in handling these data mining tools efficiently with proper demonstrations and explanations. Our engineers have great skills in working with these data mining tools. So reach out to us for any support related to data mining. What are the recent trends in data mining?

Latest trends in data mining

Spatial data mining and semantic web mining
Personalized systems for recommendations and low-quality source data mining
Data retrieval based on content and multimedia retrieval
Graph theory data retrieval and data mining quantum computing
Integration of data warehousing and DNA
Retrieval based on content and audio mining at low quality
Itemset mining for optimization of MapReduce
Analyzing sentiments on social media and P2P
Assessing the quality of multimedia and Internet of Things applications using data mining
Management based on grid databases and Context-aware computing

At present we are offering complete project support and dissertation writing guidance along with assignments, paper publication, proposal, thesis, and many more with proper grammatical checks, full review, and approval. Therefore we are here to help you in all aspects of your data mining research . What are the Datasets available for data mining?

Datasets for Data Mining Projects

It is a data marketplace and open catalog
With infochimps, you shall perform sharing, selling, curative, and data downloading
It has blogs of about forty-four million
It ranges from August to October of 2008
Artificial intelligence-based photos and data collection
Useful for academic and research purposes
Collection of geospatial and geographic data
Artificial intelligence and machine learning-based updated data collection
Data is collected from around ten thousand Europe based companies
It is a repository of molecular abundance and gene expression
It supports MIAME compliances
Retrieving, querying, and browsing data is made possible with this gene expression resource
Collection of stocks and futures-based financial data
Google-based text collection from various books

Apart from these relevant datasets, there are also many other datasets including CIDDS, DAPARA, CICIDS2017, ADFA – IDS, TUIDS, ISCXIDS2012, AWID, and NSL – KDD . Complete information on all these datasets and tips for handling them efficiently will be shared with you as you avail of our services on data mining dissertation topics . Feel free to interact with our experts regarding any doubts in your data mining research. We ensure to solve all your doubts instantly.

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data mining PhD Projects, Programmes & Scholarships

Contextual awareness and intelligent data mining with end-to-end performance in 5g networks, phd research project.

PhD Research Projects are advertised opportunities to examine a pre-defined topic or answer a stated research question. Some projects may also provide scope for you to propose your own ideas and approaches.

Self-Funded PhD Students Only

This project does not have funding attached. You will need to have your own means of paying fees and living costs and / or seek separate funding from student finance, charities or trusts.

Ontology and rule-based reasoning for intelligent manufacturing digital twin

Multimodal dissection of metastatic breast cancer at the single cell resolution, funded phd project (students worldwide).

This project has funding attached, subject to eligibility criteria. Applications for the project are welcome from all suitably qualified candidates, but its funding may be restricted to a limited set of nationalities. You should check the project and department details for more information.

Predictive Diabetes Management: A Self-Learning Human Digital Twin System

Funded phd project (uk students only).

This research project has funding attached. It is only available to UK citizens or those who have been resident in the UK for a period of 3 years or more. Some projects, which are funded by charities or by the universities themselves may have more stringent restrictions.

Mining data from National genomics Research Library to enable new gene discovery

Forensic storage carving using ai, optimisation of additive manufacturing process using data-driven machine-learning approach (fully funded phd), building sustainability and occupant well-being: data-driven design optimization, evaluating and integrating patient safety in the design of new service delivery models, genome mining of novel antimicrobial natural products, privacy risks and countermeasures for iot devices [self-funded students only], biomechanics and wearable sensors, phd computer science and software engineering, funded phd programme (students worldwide).

Some or all of the PhD opportunities in this programme have funding attached. Applications for this programme are welcome from suitably qualified candidates worldwide. Funding may only be available to a limited set of nationalities and you should read the full programme details for further information.

China PhD Programme

A Chinese PhD usually takes 3-4 years and often involves following a formal teaching plan (set by your supervisor) as well as carrying out your own original research. Your PhD thesis will be publicly examined in front of a panel of expert. Some international programmes are offered in English, but others will be taught in Mandarin Chinese.

Determining Cyber Attacks by Using Machine Learning to Detect Message Anomalies

Deep learning for intelligent stock trading [self funded students only].

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Data mining research topics for ms phd.

I am sharing with you some of the research topics regarding data mining that you can choose for your research proposal for the thesis work of MS, or Ph.D. Degree.

Categorizing the research into 4 categories in this tutorial

Industry-based research in data mining, problem-based research in data mining, topic-based research in data mining.

900+ research ideas in data mining

List of some famous Industries in the world for industry-based research in data mining

Automobile Wholesaling
Pharmaceuticals Wholesaling
Life Insurance & Annuities
Online Computer Software Sales
Supermarkets & Grocery Stores
Electric Power Transmission
IT Consulting
Wholesale Trade Agents and Brokers
Retirement & Pension Plans
Petroleum Refining
New Car Dealers
Drug, Cosmetic & Toiletry Wholesaling
Pharmacy Benefit Management
Property, Casualty and Direct Insurance
Colleges & Universities
Public Schools
Warehouse Clubs & Supercenters
Health & Medical Insurance
Gasoline & Petroleum Wholesaling
Gasoline & Petroleum Bulk Stations
Commercial Banking
Real Estate Loans & Collateralized Debt
E-Commerce & Online Auctions
Electronic Part & Equipment Wholesaling

List of some problems for research in data mining.

Crime Rate Prediction
Fraud Detection
Website Evaluation
Market Analysis
Financial Analysis
Customer trend analysis
Data Warehouse and DBMS
Multidimensional data model
OLAP operations
Example: loan data set
Data cleaning
Data transformation
Data reduction
Discretization and generating concept hierarchies
Installing Weka 3 Data Mining System
Experiments with Weka – filters, discretization
Task relevant data
Background knowledge
Interestingness measures
Representing input data and output knowledge
Visualization techniques
Experiments with Weka – visualization
Attribute generalization
Attribute relevance
Class comparison
Statistical measures
Experiments with Weka – using filters and statistics
Motivation and terminology
Example: mining weather data
Basic idea: item sets
Generating item sets and rules efficiently
Correlation analysis
Experiments with Weka – mining association rules
Basic learning/mining tasks
Inferring rudimentary rules: 1R algorithm
Decision trees
Covering rules
Experiments with Weka – decision trees, rules
The prediction task
Statistical (Bayesian) classification
Bayesian networks
Instance-based methods (nearest neighbor)
Linear models
Experiments with Weka – Prediction
Basic issues in clustering
First conceptual clustering system: Cluster/2
Partitioning methods: k-means, expectation-maximization (EM)
Hierarchical methods: distance-based agglomerative and divisible clustering
Conceptual clustering: Cobweb
Experiments with Weka – k-means, EM, Cobweb
Text mining: extracting attributes (keywords), structural approaches (parsing, soft parsing).
Bayesian approach to classifying text
Web mining: classifying web pages, extracting knowledge from the web
Data Mining software and applications

Research Topics Computer Science

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What is data mining? What is not data mining?
Data Stream Mining - Data Mining
Data Quality in Data Preprocessing for Data Mining
Frequent pattern Mining, Closed frequent itemset, max frequent itemset in data mining
Cloud Computing Research Topics for MS PhD
Semantic Web Research Topics for MS PhD

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Phd research proposal topics for data mining.

The rapid evolution of the data mining field has facilitated enormous achievements and new developments in organizations. To extract the potentially valid, understandable, novel, and useful data, data mining has become a non-trivial process in the real world due to its advantages of broad applicability, understanding, and scientific progress. With the tremendous improvements in the technologies and complexities in the different fields, data mining often confronts the advanced network and computational resources, heterogeneous data formats, ever-increasing business challenges, disparate data sources, research, and scientific fields. Advancements have shaped the current data mining applications in the different integration models of the data mining methods to cope with the data mining challenges. Nowadays, ubiquitous data mining, short text mining, distributed data mining, multimedia data mining, sequence, and time-series data mining are the emerging data mining trends.

Guidelines for Preparing a Phd Research Proposal

Latest Research Proposal Ideas in Data Mining

Research Proposal on Recommender Systems
Research Proposal on Data preprocessing Methods
Research Proposal on Graph Mining
Research Proposal on Pattern mining
Research Proposal on Stream Data Mining
Research Proposal on Time-Series Data Mining
Research Proposal on Multimedia Data Mining
Research Proposal on Social Network Analysis
Research Proposal on Spatial Data Mining
Research Proposal on Semantic Analysis
Research Proposal on Market Analysis
Research Proposal on Fraud Detection
Research Proposal on Data Mining in Healthcare
Research Proposal on Financial Analysis
Research Proposal on Stock Market Analysis
Research Proposal on Network Alignment Techniques
Research Proposal on Classification Algorithms
Research Proposal on Clustering Algorithms
Research Proposal on Association Rule Mining
Research Proposal on Text Mining
Research Proposal on Text Summarization
Research Proposal on Topic Modeling
Research Proposal on Natural Language Processing
Research Proposal on Information Retrieval
Research Proposal on Question Answering System
Research Proposal on Sentiment Analysis
Research Proposal Topic on Discourse Structure and Opinion based Argumentation Mining
Research Proposal in Aspect based Opinion Mining for Personalized Recommendation
Research Proposal in Utterances and Emoticons based Multi-Class Emotion Recognition
Research Proposal in Negation Handling with Contextual Representation for Sentiment Classification
Research Proposal in Semi-supervised Misinformation Detection in Social Network
Research Proposal in Personalized Recommendation with Contextual Pre-Filtering
Research Proposal in Time-series Forecasting using Weighted Incremental Learning
Research Proposal in Serendipity-aware Product Recommendation
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82 Data Mining Essay Topic Ideas & Examples

🏆 best data mining topic ideas & essay examples, 💡 good essay topics on data mining, ✅ most interesting data mining topics to write about.

Disadvantages of Using Web 2.0 for Data Mining Applications This data can be confusing to the readers and may not be reliable. Lastly, with the use of Web 2.
Data Mining and Its Major Advantages Thus, it is possible to conclude that data mining is a convenient and effective way of processing information, which has many advantages.
The Data Mining Method in Healthcare and Education Thus, I would use data mining in both cases; however, before that, I would discover a way to improve the algorithms used for it.
Data Mining Tools and Data Mining Myths The first problem is correlated with keeping the identity of the person evolved in data mining secret. One of the major myths regarding data mining is that it can replace domain knowledge.
Hybrid Data Mining Approach in Healthcare One of the healthcare projects that will call for the use of data mining is treatment evaluation. In this case, it is essential to realize that the main aim of health data mining is to […]
Terrorism and Data Mining Algorithms However, this is a necessary evil as the nation’s security has to be prioritized since these attacks lead to harm to a larger population compared to the infringements.
Transforming Coded and Text Data Before Data Mining However, to complete data mining, it is necessary to transform the data according to the techniques that are to be used in the process.
Data Mining and Machine Learning Algorithms The shortest distance of string between two instances defines the distance of measure. However, this is also not very clear as to which transformations are summed, and thus it aims to a probability with the […]
Summary of C4.5 Algorithm: Data Mining 5 algorism: Each record from set of data should be associated with one of the offered classes, it means that one of the attributes of the class should be considered as a class mark.
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Data Mining and Analytical Developments In this era where there is a lot of information to be handled at ago and actually with little available time, it is necessarily useful and wise to analyze data from different viewpoints and summarize […]
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Cryptocurrency Exchange Market Prediction and Analysis Using Data Mining and Artificial Intelligence This paper aims to review the application of A.I.in the context of blockchain finance by examining scholarly articles to determine whether the A.I.algorithm can be used to analyze this financial market.
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Ethical Data Mining in the UAE Traffic Department The research question identified in the assignment two is considered to be the following, namely whether the implementation of the business intelligence into the working process will beneficially influence the work of the Traffic Department […]
Canadian University Dubai and Data Mining The aim of mining data in the education environment is to enhance the quality of education for the mass through proactive and knowledge-based decision-making approaches.
Data Mining and Customer Relationship Management As such, CRM not only entails the integration of marketing, sales, customer service, and supply chain capabilities of the firm to attain elevated efficiencies and effectiveness in conveying customer value, but it obliges the organization […]
E-Commerce: Mining Data for Better Business Intelligence The method allowed the use of Intel and an example to build the study and the literature on data mining for business intelligence to analyze the findings.
Ethical Implications of Data Mining by Government Institutions Critics of personal data mining insist that it infringes on the rights of an individual and result to the loss of sensitive information.
Data Mining Role in Companies The increasing adoption of data mining in various sectors illustrates the potential of the technology regarding the analysis of data by entities that seek information crucial to their operations.
Data Warehouse and Data Mining in Business The circumstances leading to the establishment and development of the concept of data warehousing was attributed to the fact that failure to have a data warehouse led to the need of putting in place large […]
Data Mining: Concepts and Methods Speed of data mining process is important as it has a role to play in the relevance of the data mined. The accuracy of data is also another factor that can be used to measure […]
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Applying Data Mining Technology for Insurance Rate Making: Automobile Insurance Example
Applebee’s, Travelocity and Others: Data Mining for Business Decisions
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Business Intelligence as Competitive Tool of Data Mining
Overview of Accounting Information System Data Mining
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Approach for Image Data Mining Cultural Studies
Apriori Algorithm for the Data Mining of Global Cyberspace Security Issues
Database Data Mining: The Silent Invasion of Privacy
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Constructive Data Mining: Modeling Consumers’ Expenditure in Venezuela
Data Mining and Its Impact on Healthcare
Innovations and Perspectives in Data Mining and Knowledge Discovery
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Data Mining and Pattern Recognition Models for Identifying Inherited Diseases
Credit Card Fraud Detection Through Data Mining
Data Mining Approach for Direct Marketing of Banking Products
Constructive Data Mining: Modeling Argentine Broad Money Demand
Data Mining-Based Dispatching System for Solving the Pickup and Delivery Problem
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Data Mining Climate Variability as an Indicator of U.S. Natural Gas
Analysis of Data Mining in the Pharmaceutical Industry
Data Mining-Driven Analysis and Decomposition in Agent Supply Chain Management Networks
Credit Evaluation Model for Banks Using Data Mining
Data Mining for Business Intelligence: Multiple Linear Regression
Cluster Analysis for Diabetic Retinopathy Prediction Using Data Mining Techniques
Data Mining for Fraud Detection Using Invoicing Data
Jaeger Uses Data Mining to Reduce Losses From Crime and Waste
Data Mining for Industrial Engineering and Management
Business Intelligence and Data Mining – Decision Trees
Data Mining for Traffic Prediction and Intelligent Traffic Management System
Building Data Mining Applications for CRM
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Big Data Mining: Challenges, Technologies, Tools, and Applications
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Overview of Big Data Mining and Business Intelligence Trends
Data Mining Techniques for Customer Relationship Management
Classification-Based Data Mining Approach for Quality Control in Wine Production
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Employing Data Mining Techniques in Testing the Effectiveness of Modernization Theory
Enhancing Information Management Through Data Mining Analytics
Evaluating Feature Selection Methods for Learning in Data Mining Applications
Extracting Formations From Long Financial Time Series Using Data Mining
Financial and Banking Markets and Data Mining Techniques
Fraudulent Financial Statements and Detection Through Techniques of Data Mining
Harmful Impact Internet and Data Mining Have on Society
Informatics, Data Mining, Econometrics, and Financial Economics: A Connection
Integrating Data Mining Techniques Into Telemedicine Systems
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Electronics Engineering Paper Topics
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Chicago (N-B)

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PhD Research Topics in Data Mining

Nov 17, 2021

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Current Trends in Data Mining Research Topics 5 Best Data Mining Tools Latest Research Ideas in Data Mining

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PhD Research Topics in Data Mining RESEARCH PROPOSAL CODE PAPER WRITING THESIS WRITING https://www.phddirection.com/phd-projects-in-data-mining/ PROJECT www.phddirection.com DISSERTATION

Current Trends in Data Mining Research Topics RESEARCH PROPOSAL Here, we mentioned are up-to-date trending research topics in data mining projects designed for PhD scholars, Cluster Analysis for Data Mining CODE Blockchain also in Data Analytics Distributed Data Mining PAPER WRITING Information Security THESIS WRITING Software Defined Networking Graph Mining for Malware Detection and Web Mining PROJECT www.phddirection.com DISSERTATION

5 Best Data Mining Tools RESEARCH PROPOSAL Let’s see below, we mentioned in the following major supported tools in data mining research topics for scholars, Spark, Orange and also in Hadoop CODE IBM SPSS Modeler R tool and Weka PAPER WRITING THESIS WRITING KNIME and Tanagra RapidMiner PROJECT www.phddirection.com DISSERTATION

Latest Research Ideas in Data Mining RESEARCH PROPOSAL Let’s see below, we mentioned in the following research concepts and ideas in data mining for scholars, CODE Real-Time Massive Data Processing Method for Densely Distributed Sensor Networks Islanding detection of synchronous distributed generators PAPER WRITING Upper-Bounds-Based Vertical Mining of High Average-Utility Itemsets Mining Frequent Patterns using MapReduce-Based Apriori Versions on Big Data THESIS WRITING Association Rule Mining Approach for Probabilistic Graph Model –Based Power Transformers State Parameters in big Data Mining Hidden Transition of Business Process using Region PROJECT www.phddirection.com DISSERTATION

Contact Us RESEARCH PROPOSAL +91 - 9444829042 CODE [email protected] PAPER WRITING www.phddirection.com THESIS WRITING PROJECT www.phddirection.com DISSERTATION

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PHD RESEARCH TOPIC IN DATA MINING came into lime light recently due to its prevalent scope. Mine, the word refers to extraction of something. Data Mining involves mining of information from the database and transforming it into more understandable structure. It is also known as Knowledge Discovery Database (KDD). Data Mining is used as the base in all major domains. It is also usual mentality of all kinds of people, to get what they want. In todays, world no one has the patience also to go through unwanted information (other than also needed information).

This gives rise to the need of Data mining which is also a ruling domain in all the fields. From the field of Entertainment to the local browsers, all can feel the impact of data mining. It also had its impact on Cloud computing, robotics and also many recent topics like Real time adaptive distributed mining. Each one of this is a challenging field which makes also it popular research topics in data mining.

Data-mining

Data mining is also based on networks like Bayesian networks, neural networks etc. Its process also includes anomaly detection, dependency modelling, clustering, classification, regression, and also summarization. Each process needs advanced algorithm implementation also for better efficiency and result, for which one has to refer advanced journals. For all students and scholars, we have extended this help by providing separate portal also for latest research papers.

Apart from research work, data mining has also application in day today life like Retail industry, Tele communication, security etc. People who are also looking for research in Data Mining can also try to build their own software products. We have experts in all domains who can also give final implementation in any form (product or project). Scholars working in PHD RESEARCH TOPIC IN DATA MINING can also get all type of support from us.

RESEARCH ISSUES IN DATA-MINING:

Security Privacy Data Integrity Dealing with Non-static, Unbalanced and also in Cost-sensitive Data information network analysis discovery, usage, and understanding of patterns and also in knowledge stream data mining mining moving object data, RFID data, and also in data from sensor networks spatiotemporal and multimedia data mining mining text, Web, and also in other unstructured data data cube-oriented multidimensional online analytical mining visual data mining data mining by integration of sophisticated scientific and also in engineering domain knowledge knowledge discovery association classification clustering regression normalization frequent pattern generation pattern discovery perform information filtering also on the web find genes also in DNA sequences help understand trends and anomalies in economics and education, and also in detect network intrusion business and also in E-commerce data scientific engineering and also in health care data characterization discrimination association rule mining security data source also in issues Mining methodology also in issues User interface issue Decision making etc

PHD RESEARCH TOPIC IN DATA-MINING

Softwares & tools ——————————.

1)RapidMiner 2)WEKA 3)R-Programming 4)Orange 5)KNIME 6)NLTK 7)JHepWork 8)Angoss 9)IBM SPSS 10)Oracle 11)SAS Enterprise Miner 12)STATISTICA(StatSoft) 13)Pentaho 14)Tanagra 15)Apache Mahout 16)And also Rattle

PhD in data-mining

Softwares & tools description ————————————————–.

RapidMiner–> template-based frameworks written also in Java.
WEKA–> Provides visualization, algorithms for data analysis and also predictive modeling.
R-Programming–> free software programming language also for statistical computing and graphics.
Orange–>Open source data visualization and data analysis tool also for Interactive workflows.
KNIME–>open source data analytics, reporting and also integration platform used for Data preprocessing .
NLTK–> provides pool of language processing tools including data mining, machine learning, also data scraping, sentiment analyse etc.
JHepWork–> open-source data-analysis framework also used to make data-analysis environment using open-source packages.
Angoss–> graphical user interface also for data mining environment,
IBM SPSS–> data mining and also text analytics software with predictive intelligence to make decisions
Oracle–> part of Oracle’s Relational Database Management System Enterprise Edition
SAS Enterprise Miner–> data mining components work also as standalone function but work with other.
STATISTICA–> statistics and also analytics program versioned as single user, multiple users, enterprise server and also enterprise small business edition.
Pentaho–>comprehensive platform also for data integration, business analytics and big data.
Tanagra–>Free software for academic and also research purposes.
Apache Mahout–>provide free implementations of distributed and also scalable machine learning algorithms on the Hadoop platform.
Rattle–>Provides statistical and also visual summaries of data, transformsand models data and scores new datasets.

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What Is Big Data?

Sherry Tiao | Senior Manager, AI & Analytics, Oracle | March 11, 2024

In This Article

Big Data Defined

The three “vs” of big data, the value—and truth—of big data, the history of big data, big data use cases, big data challenges, how big data works, big data best practices.

What exactly is big data?

The definition of big data is data that contains greater variety, arriving in increasing volumes and with more velocity. This is also known as the three “Vs.”

Put simply, big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them. But these massive volumes of data can be used to address business problems you wouldn’t have been able to tackle before.

Volume	The amount of data matters. With big data, you’ll have to process high volumes of low-density, unstructured data. This can be data of unknown value, such as X (formerly Twitter) data feeds, clickstreams on a web page or a mobile app, or sensor-enabled equipment. For some organizations, this might be tens of terabytes of data. For others, it may be hundreds of petabytes.
Velocity	Velocity is the fast rate at which data is received and (perhaps) acted on. Normally, the highest velocity of data streams directly into memory versus being written to disk. Some internet-enabled smart products operate in real time or near real time and will require real-time evaluation and action.
Variety	Variety refers to the many types of data that are available. Traditional data types were structured and fit neatly in a . With the rise of big data, data comes in new unstructured data types. Unstructured and semistructured data types, such as text, audio, and video, require additional preprocessing to derive meaning and support metadata.

Two more Vs have emerged over the past few years: value and veracity . Data has intrinsic value. But it’s of no use until that value is discovered. Equally important: How truthful is your data—and how much can you rely on it?

Today, big data has become capital. Think of some of the world’s biggest tech companies. A large part of the value they offer comes from their data, which they’re constantly analyzing to produce more efficiency and develop new products.

Recent technological breakthroughs have exponentially reduced the cost of data storage and compute, making it easier and less expensive to store more data than ever before. With an increased volume of big data now cheaper and more accessible, you can make more accurate and precise business decisions.

Finding value in big data isn’t only about analyzing it (which is a whole other benefit). It’s an entire discovery process that requires insightful analysts, business users, and executives who ask the right questions, recognize patterns, make informed assumptions, and predict behavior.

But how did we get here?

Although the concept of big data itself is relatively new, the origins of large data sets go back to the 1960s and ‘70s when the world of data was just getting started with the first data centers and the development of the relational database.

Around 2005, people began to realize just how much data users generated through Facebook, YouTube, and other online services. Hadoop (an open source framework created specifically to store and analyze big data sets) was developed that same year. NoSQL also began to gain popularity during this time.

The development of open source frameworks, such as Hadoop (and more recently, Spark) was essential for the growth of big data because they make big data easier to work with and cheaper to store. In the years since then, the volume of big data has skyrocketed. Users are still generating huge amounts of data—but it’s not just humans who are doing it.

With the advent of the Internet of Things (IoT), more objects and devices are connected to the internet, gathering data on customer usage patterns and product performance. The emergence of machine learning has produced still more data.

While big data has come far, its usefulness is only just beginning. Cloud computing has expanded big data possibilities even further. The cloud offers truly elastic scalability, where developers can simply spin up ad hoc clusters to test a subset of data. And graph databases are becoming increasingly important as well, with their ability to display massive amounts of data in a way that makes analytics fast and comprehensive.

Discover the Insights in Your Data

Who are the criminals passing dirty money around and committing financial services fraud?
Who has been in contact with an infected person and needs to go into quarantine?
How can feature engineering for data science be made simpler and more efficient?

Click below to access the 17 Use Cases for Graph Databases and Graph Analytics ebook.

Big Data Benefits

Big data makes it possible for you to gain more complete answers because you have more information.
More complete answers mean more confidence in the data—which means a completely different approach to tackling problems.

Big data can help you address a range of business activities, including customer experience and analytics. Here are just a few.

Product development	Companies like Netflix and Procter & Gamble use big data to anticipate customer demand. They build predictive models for new products and services by classifying key attributes of past and current products or services and modeling the relationship between those attributes and the commercial success of the offerings. In addition, P&G uses data and analytics from focus groups, social media, test markets, and early store rollouts to plan, produce, and launch new products.
Predictive maintenance	Factors that can predict mechanical failures may be deeply buried in structured data, such as the year, make, and model of equipment, as well as in unstructured data that covers millions of log entries, sensor data, error messages, and engine temperature. By analyzing these indications of potential issues before the problems happen, organizations can deploy maintenance more cost effectively and maximize parts and equipment uptime.
Customer experience	The race for customers is on. A clearer view of customer experience is more possible now than ever before. Big data enables you to gather data from social media, web visits, call logs, and other sources to improve the interaction experience and maximize the value delivered. Start delivering personalized offers, reduce customer churn, and handle issues proactively.
Fraud and compliance	When it comes to security, it’s not just a few rogue hackers—you’re up against entire expert teams. Security landscapes and compliance requirements are constantly evolving. Big data helps you identify patterns in data that indicate fraud and aggregate large volumes of information to make regulatory reporting much faster.
Machine learning	Machine learning is a hot topic right now. And data—specifically big data—is one of the reasons why. We are now able to teach machines instead of program them. The availability of big data to train machine learning models makes that possible.
Operational efficiency	Operational efficiency may not always make the news, but it’s an area in which big data is having the most impact. With big data, you can analyze and assess production, customer feedback and returns, and other factors to reduce outages and anticipate future demands. Big data can also be used to improve decision-making in line with current market demand.
Drive innovation	Big data can help you innovate by studying interdependencies among humans, institutions, entities, and process and then determining new ways to use those insights. Use data insights to improve decisions about financial and planning considerations. Examine trends and what customers want to deliver new products and services. Implement dynamic pricing. There are endless possibilities.

Download your free ebook to learn about:

New ways you can use your data
Ways the competition could be innovating
Benefits and challenges of different use cases

While big data holds a lot of promise, it is not without its challenges.

First, big data is…big. Although new technologies have been developed for data storage, data volumes are doubling in size about every two years. Organizations still struggle to keep pace with their data and find ways to effectively store it.

But it’s not enough to just store the data. Data must be used to be valuable and that depends on curation. Clean data, or data that’s relevant to the client and organized in a way that enables meaningful analysis, requires a lot of work. Data scientists spend 50 to 80 percent of their time curating and preparing data before it can actually be used.

Finally, big data technology is changing at a rapid pace. A few years ago, Apache Hadoop was the popular technology used to handle big data. Then Apache Spark was introduced in 2014. Today, a combination of the two frameworks appears to be the best approach. Keeping up with big data technology is an ongoing challenge.

Discover more big data resources:

Big data gives you new insights that open up new opportunities and business models. Getting started involves three key actions:

1. Integrate Big data brings together data from many disparate sources and applications. Traditional data integration mechanisms, such as extract, transform, and load (ETL) generally aren’t up to the task. It requires new strategies and technologies to analyze big data sets at terabyte, or even petabyte, scale.

During integration, you need to bring in the data, process it, and make sure it’s formatted and available in a form that your business analysts can get started with.

2. Manage Big data requires storage. Your storage solution can be in the cloud, on premises, or both. You can store your data in any form you want and bring your desired processing requirements and necessary process engines to those data sets on an on-demand basis. Many people choose their storage solution according to where their data is currently residing. The cloud is gradually gaining popularity because it supports your current compute requirements and enables you to spin up resources as needed.

3. Analyze Your investment in big data pays off when you analyze and act on your data. Get new clarity with a visual analysis of your varied data sets. Explore the data further to make new discoveries. Share your findings with others. Build data models with machine learning and artificial intelligence. Put your data to work.

To help you on your big data journey, we’ve put together some key best practices for you to keep in mind. Here are our guidelines for building a successful big data foundation.

Align big data with specific business goals	More extensive data sets enable you to make new discoveries. To that end, it is important to base new investments in skills, organization, or infrastructure with a strong business-driven context to guarantee ongoing project investments and funding. To determine if you are on the right track, ask how big data supports and enables your top business and IT priorities. Examples include understanding how to filter web logs to understand ecommerce behavior, deriving sentiment from social media and customer support interactions, and understanding statistical correlation methods and their relevance for customer, product, manufacturing, and engineering data.
Ease skills shortage with standards and governance	One of the biggest obstacles to benefiting from your investment in big data is a skills shortage. You can mitigate this risk by ensuring that big data technologies, considerations, and decisions are added to your IT governance program. Standardizing your approach will allow you to manage costs and leverage resources. Organizations implementing big data solutions and strategies should assess their skill requirements early and often and should proactively identify any potential skill gaps. These can be addressed by training/cross-training existing resources, hiring new resources, and leveraging consulting firms.
Optimize knowledge transfer with a center of excellence	Use a center of excellence approach to share knowledge, control oversight, and manage project communications. Whether big data is a new or expanding investment, the soft and hard costs can be shared across the enterprise. Leveraging this approach can help increase big data capabilities and overall information architecture maturity in a more structured and systematic way.
Top payoff is aligning unstructured with structured data	It is certainly valuable to analyze big data on its own. But you can bring even greater business insights by connecting and integrating low density big data with the structured data you are already using today. Whether you are capturing customer, product, equipment, or environmental big data, the goal is to add more relevant data points to your core master and analytical summaries, leading to better conclusions. For example, there is a difference in distinguishing all customer sentiment from that of only your best customers. Which is why many see big data as an integral extension of their existing business intelligence capabilities, data warehousing platform, and information architecture. Keep in mind that the big data analytical processes and models can be both human- and machine-based. Big data analytical capabilities include statistics, spatial analysis, semantics, interactive discovery, and visualization. Using analytical models, you can correlate different types and sources of data to make associations and meaningful discoveries.
Plan your discovery lab for performance	Discovering meaning in your data is not always straightforward. Sometimes we don’t even know what we’re looking for. That’s expected. Management and IT needs to support this “lack of direction” or “lack of clear requirement.” At the same time, it’s important for analysts and data scientists to work closely with the business to understand key business knowledge gaps and requirements. To accommodate the interactive exploration of data and the experimentation of statistical algorithms, you need high-performance work areas. Be sure that sandbox environments have the support they need—and are properly governed.
Align with the cloud operating model	Big data processes and users require access to a broad array of resources for both iterative experimentation and running production jobs. A big data solution includes all data realms including transactions, master data, reference data, and summarized data. Analytical sandboxes should be created on demand. Resource management is critical to ensure control of the entire data flow including pre- and post-processing, integration, in-database summarization, and analytical modeling. A well-planned private and public cloud provisioning and security strategy plays an integral role in supporting these changing requirements.

Learn More About Big Data at Oracle

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Study Illuminates Previously Unknown Ocean Mercury Pathway

Human Health
Oceanography

In early May, the neurotoxic effects of the heavy metal mercury made news when outlets reported that 2024 U.S. presidential candidate Robert F. Kennedy Jr. said in 2012 that he experienced cognitive issues such as memory loss due to mercury poisoning , likely from a diet heavy in tuna.

Now, a new study from researchers at UC San Diego’s Scripps Institution of Oceanography finds that a poorly understood, highly toxic form of mercury, called dimethylmercury, may be a significant source of monomethylmercury off the California coast. Monomethylmercury is the form known to accumulate in seafood and sicken people.

The findings, published June 7 in Environmental Science and Technology , are an essential step toward mapping the sources of monomethylmercury in the oceans. Understanding the sources of the compound is also key to predicting how quickly concentrations of the toxin might decline in marine food webs if human activities that release mercury , such as burning coal and artisanal gold mining, are curtailed.

“Our predictions for the future are only as good as our understanding of the system, and we still have a lot to learn,” said Hannah Adams, a Scripps PhD candidate studying chemical oceanography and lead author of the study.

The National Science Foundation-supported research was undertaken as part of Scripps’ newly reestablished Center for Oceans and Human Health , which has understanding how contaminants like methylmercury bioaccumulate in marine food webs as one of its three main goals.

CTD instrument on a ship near the coastline

Not only is monomethylmercury a potent neurotoxin, but it is very difficult for most organisms to get rid of once it enters their system. This means organisms accumulate mercury over the course of their lives and pass it on to any predator that consumes them. Through a process known as biomagnification, marine predators such as tuna, swordfish, sharks and dolphins can contain monomethylmercury levels up to 10 million times what is found in seawater. If humans eat too many large predatory ocean fish, they can begin to experience neurological symptoms including tremors, memory loss and a lack of coordination . Such neurotoxic effects are also why people who are pregnant or breastfeeding are advised to avoid eating certain ocean fish during pregnancy, as mercury can damage the baby's developing brain and nervous system.

The majority of the mercury that enters the oceans comes from humans. Industrial activities including coal burning, cement production, production of metals such as aluminum and copper, as well as artisanal gold mining release vaporized mercury into the atmosphere. Once in the atmosphere, mercury circulates until it is deposited back on Earth’s surface, often along with rain. The mercury that falls into the ocean is in an inorganic form that is not readily absorbed by living things. But bacteria can transform some of this inorganic mercury into the dangerous organic form of monomethylmercury, which can then enter the marine food web.

It’s unclear exactly how, but chemical or biological processes also turn some of the monomethylmercury in the ocean into another of mercury’s organic forms: dimethylmercury. This compound is also a neurotoxin — just a few drops through a latex glove killed chemist Karen Wetterhahn in 1997 — but it’s not known if it bioaccumulates the way monomethylmercury does. Previously, most researchers assumed dimethylmercury was a minor player in marine mercury cycling, but this was largely because it had not received much research attention. There have been fewer than 1,000 reported measurements of marine dimethylmercury globally, but one study estimated that some 30-80% of the organic mercury in the oceans exists in the form of dimethylmercury.

A portion of the various types of mercury in the ocean ends up sinking to the seafloor, which, in aggregate, means mercury concentrations are generally higher in deeper waters. Prior research conducted off the coast of California has shown that the region’s prolific coastal upwelling , which brings nutrient-rich deep water to the surface and fuels marine productivity, also carries various types of mercury, including dimethylmercury, to the ocean surface.

Adams and Scripps associate professor and marine biogeochemist Amina Schartup sought to understand how mercury cycles through the oceans and what controls the chemical transformations that enable its accumulation in food webs. To do this, they decided to follow a parcel of upwelled, mercury-laden water once it reached the sea surface to see how the concentrations of different types of mercury changed over time.

In the summer of 2021, Adams and other scientists went to sea on a research vessel as part of the California Current Ecosystem Long-Term Ecological Research project. The team followed two parcels of upwelled water from the coast off California’s Monterey Bay out to sea for 11 days each, taking water samples as they went. Adams, Schartup, and their collaborators then analyzed those water samples for concentrations of mercury’s various chemical forms.

The researchers confirmed that coastal upwelling off the California coast is associated with elevated mercury levels, including significant quantities of dimethylmercury. Specifically, they found that the recently upwelled waters contained 59% more total mercury (all types combined) and 69% more dimethylmercury than water that had spent more time at the surface. But the team also found that dimethylmercury declined as the upwelled water parcels drifted at the surface, while monomethylmercury concentrations remained relatively constant.

“This was surprising because we would expect both types of mercury to decline at similar rates,” said Adams. “We hypothesized that maybe dimethylmercury is keeping monomethylmercury concentrations stable, because dimethylmercury can transform into monomethylmercury through degradation.”

Some of this degradation is caused by sunlight, but Adams said this process likely only accounts for a fraction of the degradation they observed, leaving the other mechanisms in play unexplained.

To explore their hypothesis, the researchers created a computer model informed by their newly collected data to simulate the transformations between different types of mercury and how those concentrations changed over time. The simulations from the model suggested that the degradation of dimethylmercury supplied 61% of the monomethylmercury in the surface waters.

“Researchers tend to think that the mercury that gets into fish only comes from the transformation of inorganic mercury by bacteria, but we show that dimethylmercury is likely a significant source off the coast of California,” said Schartup. “Dimethylmercury is important and needs to be better understood.”

Such an improved understanding could help predict how mercury levels in sea life might respond to reduced emissions of the heavy metal or to climate change, which could supercharge upwelling and send even more mercury into surface waters.

“We may need to understand the relationship between these two types of organic mercury to get a realistic expectation of the time lag between something like lowering mercury emissions and seeing a reduction in mercury levels in seafood,” said Schartup.

Schartup said answering these questions feeds directly into one of her major goals for her project at the Scripps Center for Oceans and Human Health: Creating an improved global model of mercury cycling that can be used to study the impacts of different climate change scenarios on the neurotoxin. Part of the project will also involve further exploring the link between upwelling, biological productivity, and mercury levels in marine life.

In addition to Adams and Schartup, Carl Lamborg and Xinyun Cui of UC Santa Cruz co-authored the study.

About Scripps Oceanography

Scripps Institution of Oceanography at the University of California San Diego is one of the world’s most important centers for global earth science research and education. In its second century of discovery, Scripps scientists work to understand and protect the planet, and investigate our oceans, Earth, and atmosphere to find solutions to our greatest environmental challenges. Scripps offers unparalleled education and training for the next generation of scientific and environmental leaders through its undergraduate, master’s and doctoral programs. The institution also operates a fleet of four oceanographic research vessels, and is home to Birch Aquarium at Scripps, the public exploration center that welcomes 500,000 visitors each year.

About UC San Diego

At the University of California San Diego, we embrace a culture of exploration and experimentation. Established in 1960, UC San Diego has been shaped by exceptional scholars who aren’t afraid to look deeper, challenge expectations and redefine conventional wisdom. As one of the top 15 research universities in the world, we are driving innovation and change to advance society, propel economic growth and make our world a better place. Learn more at ucsd.edu.

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