CSC492/CSC592 Homepage
Knowledge Discovery and Data Mining
CSC492/CSC592 - Fall 2005
Description:
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.
Announcements:
[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:
Assignments:
Instructor:
Dr. Lutz
Hamel
Tyler Hall, Room 251
Office Hours: TBA
email: hamel@cs.uri.edu
TA:
TBA
Tyler Hall
Office Hours: TBA
email: TBA