Clustering algorithms in Web systems and e-business ITI1102d

Course content:
1.Systems of analysis of www data used in existing applications
2.Recommender systems. Collaborative filtering methods.
3.Clustering methods in recommender systems.
4.Partitioning and hierarchical clustering methods.
5.Clustering techniques: density-based, grid-based and model-based.
6.Data preprocessing. Similarity measures. Evaluation of results.
7.Web usage mining.
8.Adaptive WWW servers.
9.Clustering of html documents.
10.Search results recommendations.
11.Clustering of search results.
12.Data mining in analysis financial data.
13.User's reputation systems used in auctions services.
14.Social networks
15.Non-contextual and contextual rank of www pages

Learning outcomes:
Knowledge about new clustering techniques and collaborative filtering methods and their application in Web systems. Acquirement of ability to process and analysis WWW serwers log files.