MET CS 699 A1 - Principles of Data Mining - Spring 2005
(Monday, Main Campus, 6:00-9:00 PM)


Suresh Kalathur, Ph.D.
Assistant Professor
Computer Science Department
Metropolitan College
Boston University
E-mail: URL:

Required Textbook & Resources


Course Web Site


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Course Overview

Data mining and investigation is a key goal behind any data warehouse effort. The course provides an introduction to concepts behind data mining, text mining, and web mining. The course surveys various data mining applications, methodologies, techniques, and models. Topics include decision tables, neural networks, decision trees, classification rules, association rules, clustering, statistical modeling, and linear models. The course wraps up with data mining case studies using large data sets taken from real-world projects. Algorithms will be tested on data sets using the Weka Data mining software and DBMiner.


Student Academic Conduct Code

Course Schedule





1 1/24 Introduction, Data Mining, Data Warehousing, and OLAP
2 1/31 Review of Data Warehouse architectures, Data Mining Tools
3 2/7 Data Preparation
4 2/14 Concept Description
5 2/21 Mining Association Rules University Holiday, We will have online class or regular class based on students convenience
2/28 No Lecture Schedule Makeup
6 3/7 Makeup for 2/28
7 3/14 Mid Term
8 3/28 Classification and Prediction
9 4/4 Cluster Analysis
10 4/11 Hierarchical Analysis
11 4/20 (Wed) Text Mining
12 4/25 Web Mining
13 5/2 Applications Class Presentations
14 5/9 Applications Class Presentations 

Final Project Due

Course Grading

The actual grade will be determined based on the performance in the home works, mid term, class presentation, and the final project. The percentage of each component relative to the total grade is: Homeworks (30%), Mid Term (30%), In Class Participation and Presentations (10%), and Final Project (30%)