Project2

=**CSE652: Knowledge Discovery and Data Mining**= =**Project # 2**=

** Due Date/Presentation: May 05, 2010 **

 * Implement the following clustering algorithms
 * K-Means
 * Kohonen Self-Organizing Map
 * Adaptive Resonance Theory
 * Using Mushroom, Diabetes or any other data set, compare the performance of your implementation with the techniques available in KNIME, Weka, RapidMiner and SQL Server.
 * You can take the assumption that all the attributes are categorical. If they are not, then either use your Project 1 implementation or Weka to categorize continuous data attributes.
 * Use your relevant feature selection implementation (unsupervised learning) of Project 1 to remove irrelevant columns and analyze the performance of your clustering algorithms both before and after this removal process.