CSC492/CSC592 Homepage

Knowledge Discovery and Data Mining

CSC492/CSC592 - Fall 2005


Our ability to collect and store data vastly outstrips out current ability to analyze  data and derive useful information from it.  The field of data mining holds some promise to provide relief in this area.  

This course provides an overview of Knowledge Discovery and Data Mining (KDD). KDD deals with data integration techniques and with the discovery, interpretation and visualization of patterns in large collections of data. Topics covered in this course include data mining methods such as rule-based learning, decision trees, association rules and neural-networks; data visualization; and the cross industry standard process for data mining (CRISP-DM). The work discussed originates in the fields of artificial intelligence, machine learning, statistical data analysis, data visualization, databases, and information retrieval. Several scientific and industrial applications of KDD will be described. In particular, current applications to bioinformatics, e-commerce, and web mining will be studied.

In addition to the course work described above, students will also be required to complete several projects using the Weka data mining tool set.  We will be using Weka Version 3.4.


[11/28/05] Posted SOM tutorial.
[11/28/05] Posted Assignment #7.
[11/8/05] Posted Assignment #5.
[10/12/05] Posted Assignment #4.
[9/25/05] Posted Assignment #3.
[9/20/05] Posted Assignment #2.
[9/15/05] Posted Assignment #1.
[8/27/05] Welcome! If you haven't done so yet, please download Weka 3.4 and install it on your machine.

Documents of Interest:



Dr. Lutz Hamel
Tyler Hall, Room 251
Office Hours: TBA


Tyler Hall
Office Hours: TBA
email: TBA