Syllabus

Introduction to Data mining: Motivation for Data Mining, its importance, Role of Data in Data Mining, Data Mining functionalities, patterns in data mining, Type of patterns, Classification of Data Mining Systems, Major issues in Data Mining Data Warehousing and OLTP technology for Data Mining, Data Mining Languages, and System Architectures, Concept Description: Characterization and Comparison, Mining Association Rules in Large Databases, Classification and Prediction, Cluster Analysis, Mining Complex Data, Applications and Trends in Data Mining. Characteristics of data warehouse, Data Mart, Online Analytical Processing, OLAP tools, Data warehouse Architecture, Organizational Issuer, Tools for Data warehousing, Performance consideration, case studies.

To provide students with basic concepts in Data Mining and Data Warehousing

To make the students understand the basic and state-of-the algorithms used for analyzing data obtained from different sources

To build a warehouse and demonstrate competence with the fundamental tasks involved with it.

• Aware of various critical operations involved in designing a data warehouse in many application domains.
• Implement well-known data mining techniques for obtaining interesting knowledge from data.
• Formulate new techniques for analyzing complex data.

Pang-Ning Tan, Vipin Kumar and Michael Steinbach, Introduction to data mining, Pearson, 2007

Ian H. Witten and Eibe Frank, , Data Mining: Practical Machine Learning Tools and Techniques, Elsevier, 2008.

Jiawei Han, Micheline Kamber and Jian Pei, Data Mining: Concepts and Techniques, Morgan Kaufmann, 2006 , xxxxxx