This 3 credit course will cover the fundamentals of data mining from both the database and pattern recognition perspectives. Also, we will study how mining can work in semi-structured environments such as the web. Emphasis will be placed on both understanding the basics as well as the new and emerging technologies. While a text may be prescribed, most of the reading will be from papers. There will be a significant  group project.

Prerequisites: CSEE senior or graduate (or equivalent). Must have UG level background in either databases (e.g. CMSC 461) or AI/Pattern Recognition (e.g. CMSC471) . Talk to the instructor if you would like to take the course, but are not sure of your background.

Likely Topics:

• Quick brushup of basics -- relational databases, normalized data, query optimization, outer joins etc.
• Basics of Data Warehousing and OLAP
• Data Mining
• Clustering, Association, Sequences
• Study of Major Algorithms
• Overview of available tools
• Parallel/High Performance methods
• Visualization
• Web Mining
• Problems and Challenges
• Relation to "personalizing"
• Differences from regular data mining
• Study of some recently proposed approaches
• Privacy and Security concerns

Instructor: Prof. Anupam Joshi (joshi@cs.umbc.edu, 455-2590)