This course introduces students to the process and main techniques in data mining, including association rule learning; classification approaches such as inductive inference of decision trees and neural network learning, clustering techniques, and research topics such as inductive logic programming / multi-relational data mining and time series mining. The emphasis will be on algorithmic issues and data mining from a data management and machine learning viewpoint. For more information, please see http://www.cs.purdue.edu/homes/clifton/cs590d

Usually Offered:	Spring of 2005-06
Credit:	3 hours (class)
Prerequisite:	Undergraduate-level expertise in database, algorithms, and statistics; Java programming experience. Students without this background should discuss their preparation with the instructor
Restriction:	Probably not appropriate for students who have taken ECE 632
University Catalog:	CS 590D
Schedule:	Spring 2006 Instructor: Christopher Clifton