Fall 2001
DATA MINING and KNOWLEDGE DISCOVERY
CSI 709/SYST 781/INFS 781/STAT 781��
Ryszard
S. Michalski
http://www.mli.gmu.edu/michalski.html
Overview
This course covers principles, methods, and practical tools
for deriving understandable and actionable knowledge from databases, and other
information sources. Due to an explosive growth of databases in sciences and
other domains of human activity, an automated derivation of useful knowledge
from data represents one of the most important directions of computational
sciences and information technology. Students will learn through lectures,
studying assigned or self-selected reading, making presentations, and
conducting projects in the areas of their interest.
1.
Motivation and goals
2.
Fundamental
concepts�
3.
Issues in knowledge
discovery
4.
Databases and
warehouses
5.
Data selection and
preparation
6.
Statistics-based
methods
7.
Machine
learning-based and other methods
8.
Integrated data mining
systems
9.
Frontier research and
future directions
Texts:
R. S. Michalski, �Lecture
Notes on Data Mining and Knowledge Discovery,�� GMU, Fall 2001.
J. Han and M. Kamber, Data
Mining: Concepts and Techniques, Morgan Kaufmann, 2001.