MET CS 699 A1 - Principles of
Data Mining - Spring 2005
(Monday, Main Campus, 6:00-9:00 PM)
Suresh Kalathur, Ph.D.
Computer Science Department
E-mail: firstname.lastname@example.org URL: http://people.bu.edu/kalathur
Required Textbook & Resources
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 (webct.bu.edu) to enter WebCT site for this course.
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.
|1||1/24||Introduction, Data Mining, Data Warehousing, and OLAP|
|2||1/31||Review of Data Warehouse architectures, Data Mining Tools|
|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|
|8||3/28||Classification and Prediction|
|11||4/20 (Wed)||Text Mining|
Final Project Due
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%)