To acquaint students with the applications of web mining
Data mining and knowledge discovery, The KDD process, Data preparation for knowledge discovery, Introduction of various data mining techniques (Clustering, Classification, and Association rule mining), Supervised, semisupervised and unsupervised learning,
WWW, Web Mining, Web mining and Data mining, Types of web mining (Content, Usage and Structure), Types of data: Structured and Unstructured Data, Sources of Data, Stages of web mining (Preprocessing, Pattern discovery and analysis), Privacy Tradeoff.
Web Link Mining, Hyperlink based Ranking, Page Rank, Link-Based Similarity Search -Enhanced Techniques for Page Ranking, Implementation Issues, Web Crawlers
Click stream Analysis, Web Server Log Files, Pattern discovery and pattern analysis techniques (Session and Visitor Analysis, Cluster Analysis (K means clustering) and Visitor Segmentation, Association and Correlation Analysis, Analysis of Sequential Patterns, Classification and Prediction based on Web (Decision tree))
Personalized Customer Experience in B2C E-commerce, Web Search, Web wide user tracking, Auction Sites, Information Retrieval systems, Targeted marketing.