ieee data mining projects coimbatore

Why Data Mining - Machine Learning Projects ?

Data mining is a cornerstone of analytics, helping you develop the models that can uncover connections within millions or billions of records. Data mining is the process of sorting through large data sets to identify patterns and establish relationships to solve problems through data analysis. Data mining tools allow enterprises to predict future trends. Data mining is the process of finding anomalies, patterns and correlations within large data sets to predict outcomes. Using a broad range of techniques, you can use this information to increase revenues, cut costs, improve customer relationships, reduce risks and more. Here is the list of important areas where data mining is widely used : Future Healthcare , Market Basket Analysis , Education , Manufacturing Engineering , Customer Relationship Management , Fraud Detection , Intrusion Detection , Customer Segmentation , Financial Banking , Corporate Surveillance , Research Analysis , Criminal Investigation and Bio Informatics.

Latest 2018 Data Mining Machine Learning Projects Titles

Project Code Project Title
DM2018001 Exploring Hierarchical Structures for Recommender Systems
DM2018002 Longest Increasing Subsequence Computation over Streaming Sequences
DM2018003 Multi-instance Learning with Discriminative Bag Mapping
DM2018004 Multi-Label Learning with Global and Local Label Correlation
DM2018005 Profit Maximization for Viral Marketing in Online Social Networks: Algorithms and Analysis
DM2018006 Range Queries on Multi-Attribute Trajectories
DM2018007 RNN-DBSCAN: A Density-based Clustering Algorithm using Reverse Nearest Neighbor Density Estimates
DM2018008 Scalable Content-Aware Collaborative Filtering for Location Recommendation
DM2018009 Space Filling Approach for Distributed Processing of Top-k Dominating Queries
DM2018010 Structure Based User Identification across Social Networks
DM2018011 Supervised Topic Modeling using Hierarchical Dirichlet Process-based Inverse Regression: Experiments on E-Commerce Applications
DM2018012 Heterogeneous Metric Learning of Categorical Data with Hierarchical Couplings
DM2018013 A Comprehensive Study on Social Network Mental Disorders Detection via Online Social Media Mining
DM2018014 Heterogeneous Metric Learning of Categorical Data with Hierarchical Couplings
DM2018015 Leveraging Conceptualization for Short-Text Embedding
DM2018016 Multi-View Missing Data Completion
DM2018017 Road Traffic Speed Prediction: A Probabilistic Model Fusing Multi-Source Data
DM2018018 Sampling and Reconstruction Using Bloom Filters
DM2018019 A Deep Learning-Based Data Minimization Algorithm for Fast and Secure Transfer of Big Genomic Datasets
DM2018020 A Fast Parallel Community Discovery Model on Complex Networks Through Approximate Optimization
DM2018021 A Survey of Location Prediction on Twitter
DM2018022 A Two-Phase Algorithm for Differentially Private Frequent Subgraph Mining
DM2018023 AMDO: an Over-Sampling Technique for Multi-Class Imbalanced Problems
DM2018024 Capturing the Spatiotemporal Evolution in Road Traffic Networks
DM2018025 CRAFTER: a Tree-ensemble Clustering Algorithm for Static Datasets with Mixed Attributes and High Dimensionality
DM2018026 Health Monitoring on Social Media over Time
DM2018027 Link Weight Prediction Using Supervised Learning Methods and Its Application to Yelp Layered Network
DM2018028 Making a Small World Smaller: Path Optimization in Networks
DM2018029 On Generalizing Collective Spatial Keyword Queries
DM2018030 On Power Law Growth of Social Networks
DM2018031 Privacy Characterization and Quantification in Data Publishing
DM2018032 Privacy Enhanced Matrix Factorization for Recommendation with Local Differential Privacy
DM2018033 Querying a Collection of Continuous FunctionsUnsupervised Coupled Metric Similarity for Non-IID Categorical Data