MET CS 699 A1-- Principles of Data Mining (Spring 2007)


Suresh Kalathur, Ph.D.
Assistant Professor, Computer Science Dept.
Boston Univeristy Metropolitan College
808 Commonwealth Ave, Room 250
Boston, MA 02215

Phone: 617-358-0006
Fax: 617-353-2367

Course Description

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 classification, association rules, clustering, decision trees, neural networks, 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 Microsoft SQL Server 2005 (Business Intelligence Development Studio).

The course grading will consist of analyzing a series of data mining problems, a mid term exam (open text), a final programming project and presentation.

Course Grading Policy

The course grade will be based on active class participation (10%), assignments (30%), mid term exam (30%), final project and presentation (30%). Assignments and projects are expected to be submitted by their respective due dates. Late submission grades will be scaled with respect to the minimum grade of those submitted on time.

Course Web Site

All course materials will be posted using BU's WebCT site. This requires all students to have an account with the BU computer system. Click here for instructions if you require an account.

Click here to enter WebCT site for this course.


Student Conduct Code

Please review the academic conduct code

Tentative Course Schedule






1 1/22 Introduction, Data Mining, Data Warehousing, and OLAP
2 1/29 Review of Data Warehouse architectures, Data Mining Tools  
3 2/5 Data Preparation and Exploration
4 2/12 Classification  
5 2/20(Tue) Classification  
6 2/26 Association Analysis  
7 3/5 Association Analysis  
3/12 Spring Recess (No Class)
8 3/19 Clustering  
9 3/26 Mid Term  
10 4/2 Anamoly Detection, Regression Techniques  
11 4/9 Text Mining
12 4/18 (Wed) Web Mining
13 4/23 Applications Class Presentations
14 4/30 Applications

Class Presentations, Final Project Due