Introduction, Applications, issues in data mining, data warehouse, dimensional modeling, Online Analytical Processing (OLAP), data warehousing to data mining, data mining tasks, association rules, advanced association rules, classification, different approaches for classification, prediction, clustering, outlier analysis, mining spatial databases, temporal databases, mining time series and sequence data, mining world wide web.
Scope and Objectives
The course explores the concepts and techniques of data mining, a promising and flourishing frontier in database systems. Data Mining is automated extraction of patterns representing knowledge implicitly stored in large databases, data warehouses, and other massive information repositories. It is a decision support tool that addresses unique decision support problems that cannot be solved by other data analysis tools such as Online Analytical Processing (OLAP). The course covers data mining tasks like constructing decision trees, finding association rules, classification, and clustering. The course is designed to provide students with a broad understanding in the design and use of data mining algorithms. The course also aims at providing a holistic view of data mining. It will have database, statistical, algorithmic and application perspectives of data mining.
Prescribed Text Book
Tan, Pang-Ning and other “Introduction to Data Mining" Pearson Education.