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Latest IEEE Data Mining Projects

​DHS Informatics believes in students’ stratification, we first brief the students about the technologies and type of Data Mining projects and other domain projects. After complete concept explanation of the IEEE Data Mining projects, students are allowed to choose more than one IEEE Data Mining projects for functionality details. Even students can pick one project topic from Data Mining and another two from other domains like Data Mining, data mining, image process, information forensic, big data, Data Mining, Data Mining, data science, block chain etc. DHS Informatics is a pioneer institute in Bangalore / Bengaluru; we are supporting project works for other institute all over India. We are the leading final year project centre in Bangalore / Bengaluru and having office in five different main locations Jayanagar, Yelahanka, Vijayanagar, RT Nagar & Indiranagar.

We allow the ECE, CSE, ISE final year students to use the lab and assist them in project development work; even we encourage students to get their own idea to develop their final year projects for their college submission.

DHS Informatics first train students on project related topics then students are entering into practical sessions. We have well equipped lab set-up, experienced faculties those who are working in our client projects and friendly student coordinator to assist the students in their college project works.

We appreciated by students for our Latest IEEE projects & concepts on final year Data Mining projects for ECE, CSE, and ISE departments.

Latest IEEE 2018-2019 projects on Data Mining with real time concepts which are implemented using Java, MATLAB, and NS2 with innovative ideas. Final year students of computer Data Mining, computer science, information science, electronics and communication can contact our corporate office located at Jayanagar, Bangalore for Data Mining project details.

Responsive Table
IEEE DATAMINING PROJECTS
P.CODE TITLES BASEPAPER SYNOPSIS LINKS
DHS_DM_1801 IEEE 2018:A Data Mining based Model for Detection of Fraudulent Behaviour in Water Consumption BasePaper Synopsis Link
DHS_DM_1801 IEEE 2018:A Framework for Real-Time Spam Detection in Twitter BasePaper Synopsis Link
DHS_DM_1801 IEEE 2018:Collaborative Filtering Algorithm Based on Rating Difference and User Interest BasePaper Synopsis Link
DHS_DM_1801 IEEE 2018:Serendipitous Recommendation in E-Commerce Using Innovator-Based Collaborative Filtering BasePaper Synopsis Link
DHS_DM_1801 IEEE 2018:A Novel Mechanism for Fast Detection of Transformed Data Leakage BasePaper Synopsis Link
DHS_DM_1801 IEEE 2018:A Collaborative Filtering Recommender System in Primary Care: Towards a Trusting Patient-Doctor Relationship BasePaper Synopsis Link
DHS_DM_1801 IEEE 2018:A Workflow Management System for Scalable Data Mining on Clouds BasePaper Synopsis Link
DHS_DM_1801 IEEE 2018:Machine Learning Methods for Disease Prediction with Claims Data BasePaper Synopsis Link
DHS_DM_1801 IEEE 2018:Review Spam Detection using Machine Learning BasePaper Synopsis Link
DHS_DM_1801 IEEE 2018:Harnessing Multi-source Data about Public Sentiments and Activities for Informed Design BasePaper Synopsis Link
DHS_DM_1801 IEEE 2018:Classification Of A Bank Data Set On Various Data Mining Platforms Bir Banka Musteri Verilerinin Farkli VeriMadenciligi Platformlarinda Siniflandirilmasi BasePaper Synopsis Link
DHS_DM_1801 IEEE 2018:Correlated Matrix Factorization for Recommendation with Implicit Feedback BasePaper Synopsis Link
DHS_DM_1801 IEEE 2018:Heterogeneous Information Network Embedding for Recommendation BasePaper Synopsis Link
DHS_DM_1801 IEEE 2018:Deep Air Learning: Interpolation, Prediction, and Feature Analysis of Fine-grained Air Quality BasePaper Synopsis Link
DHS_DM_1701 IEEE 2017:SociRank: Identifying and Ranking Prevalent NewsTopics Using Social Media Factors BasePaper Synopsis Link
DHS_DM_1701 IEEE 2017:NetSpam: a Network-based Spam Detection Framework for Reviews in Online Social Media BasePaper Synopsis Link
DHS_DM_1701 IEEE 2017:SocialQ&A: An Online Social Network Based Question and Answer System BasePaper Synopsis Link
DHS_DM_1701 IEEE 2017:Modeling Urban Behavior by Mining Geotagged Social Data BasePaper Synopsis Link
DHS_DM_1701 IEEE 2017:A Workflow Management System for Scalable Data Mining on Clouds BasePaper Synopsis Link
DHS_DM_1601 IEEE 2016:SPORE: A Sequential Personalized Spatial Item Recommender System BasePaper Synopsis Link
DHS_DM_1601 IEEE 2016:Inverted Linear Quadtree: Efficient Top K Spatial Keyword Search BasePaper Synopsis Link
DHS_DM_1601 IEEE 2016: Truth Discovery in Crowd sourced Detection of Spatial Events BasePaper Synopsis Link
DHS_DM_1601 IEEE 2016: Sentiment Analysis of Top Colleges in India Using Twitter Data BasePaper Synopsis Link
DHS_DM_1601 IEEE 2016: FRAppE: Detecting Malicious Facebook Applications BasePaper Synopsis Link
DHS_DM_1601 IEEE 2016: Practical Approximate k-Nearest Neighbor Queries with Location and Query Privacy BasePaper Synopsis Link
DHS_DM_1601 IEEE 2016: A Novel Pipeline Approach for Efficient Big Data Broadcasting BasePaper Synopsis Link
DHS_DM_1601 IEEE 2016:VoteTrust: Leveraging Friend Invitation Graph to Defend against Social Network Sybils BasePaper Synopsis Link
DHS_DM_1601 IEEE 2016:A Secure and Dynamic Multi-Keyword Ranked Search Scheme over Encrypted Cloud Data BasePaper Synopsis Link
DHS_DM_1601 IEEE 2016:SmartCrawler: A Two-Stage Crawler for Efficiently Harvesting Deep- Web Interfaces BasePaper Synopsis Link
DHS_DM_1601 IEEE 2016: FiDoop: Parallel Mining of Frequent Itemsets Using MapReduce BasePaper Synopsis Link
DHS_DM_1501 IEEE 2015: Discover the Expert: Context-Adaptive Expert Selection for Medical Diagnosis BasePaper Synopsis Link
DHS_DM_1501 IEEE 2015: Active Learning for Ranking through Expected Loss Optimization BasePaper Synopsis Link
DHS_DM_1501 IEEE 2015:k-Nearest Neighbor Classification over Semantically Secure Encrypted Relational Data BasePaper Synopsis Link
DHS_DM_1501 IEEE 2015: Generating Searchable Public-Key Ciphertexts With Hidden Structures for Fast Keyword Search BasePaper Synopsis Link
DHS_DM_1501 IEEE 2015: Research Directions for Engineering Big Data Analytics Software BasePaper Synopsis Link
DHS_DM_1501 IEEE 2015:Co-Extracting Opinion Targets and Opinion Words from Online Reviews Based on the Word Alignment Model BasePaper Synopsis Link
DHS_DM_1501 IEEE 2015: Constructing a Global Social Service Network for Better Quality of Web Service Discovery BasePaper Synopsis Link
DHS_DM_1501 IEEE 2015: Privacy-Preserving Detection of Sensitive Data Exposure BasePaper Synopsis Link
DHS_DM_1501 IEEE 2015: Friendbook: A Semantic-Based Friend Recommendation System for Social Networks BasePaper Synopsis Link
DHS_DM_1501 IEEE 2015: Secure Distributed Deduplication Systems with Improved Reliability BasePaper Synopsis Link
DHS_IOT_1701 IEEE 2015:Constructing a Global Social Service Network for Better Quality of Web Service Discovery BasePaper Synopsis Link


DATA MINING

TECHNICS USED FOR DATA MINING

Anomaly detection (outlier/change/deviation detection) – The identification of unusual data records, that might be interesting or data errors that require further investigation.
Association rule learning (dependency modelling) – Searches for relationships between variables. For example, a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to as market basket analysis.
Clustering – is the task of discovering groups and structures in the data that are in some way or another “similar”, without using known structures in the data.
Classification – is the task of generalizing known structure to apply to new data. For example, an e-mail program might attempt to classify an e-mail as “legitimate” or as “spam”.
Regression – attempts to find a function which models the data with the least error that is, for estimating the relationships among data or datasets.
Summarization – providing a more compact representation of the data set, including visualization and report generation.

DATA MINING OPERATIONS

Link Analysis links between individuals rather than characterising whole
Predictive Modelling (supervised learning) use observations to learn to predict
Database Segmentation (unsupervised learning) partition data into similar groups.

DHS-IEEE Projects SINCE 15 YEARS

More IEEE Data Mining Project List 2016 : View | Download

Data mining is mining knowledge from data, Involving methods at the intersection of machine learning, statistics, and database systems. Its the powerful new technology with great potential to help companies focus on the most important information in their data warehouses. We have the best in class infrastructure, lab set up , Training facilities, And experienced research and development team for both educational and corporate sectors.

Data mining is the process of searching huge amount of data from different aspects and summarize it to useful information. Data mining is logical than physical subset. Our concerns usually implicate mining and text based classification on Data mining projects for Students.

The usages of variety of tools associated to data analysis for identifying relationships in data are the process for data mining. Our concern support data mining projects for IT and CSE students to carry out their academic research projects.

Data mining is the process of searching huge amount of data from different aspects and summarize it to useful information. Data mining is logical than physical subset. Our concerns usually implicate mining and text based classification on Data mining projects for Students. The usages of variety of tools associated to data analysis for identifying relationships in data are the process for data mining. Our concern support data mining projects for IT and CSE students to carry out their academic research projects.

DATA MINING Projects Process

The Data Mining process is commonly defined with the stages:


Selection
Pre-processing
Transformation
Data mining
Interpretation/evaluation

Latest

​DHS Informatics providing latest 2018-2019 IEEE projects on networking for the final year engineering students. DHS Informatics trains all students to develop their project with good idea what they need to submit in college to get good marks. DHS Informatics offers placement training in Bangalore and the program name is OJT – On Job Training, job seekers as well as final year college students can join in this placement training program and job opportunities in their dream IT companies. We are providing IEEE projects for B.E / B.TECH, M.TECH, MCA, BCA, DIPLOMA students from more than two decades.

DHS Informatics provides a latest IEEE projects for final year CSE and ISE students on Data Mining Project .   Data mining becoming a best technology to store and retrieve the data we are developing a best project on data mining domain.


Find below our latest IEEE 2018-2019 Data Mining project listType your paragraph here.