Instructor

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.

References

• Introduction to Data Mining, Marty Hall and Larry Brown, Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Publisher: Addison-Wesley, 2005. ISBN: 0-321-32136-7 (Required)
• Data Mining: Practical Machine Learning Tools and Techniques, I.H. Witten and E. Frank, SECOND EDITION, Morgan Kaufmann, 2005. , ISBN: 0-12-088407-0 (Reference)
• Data Mining with SQL Server, Z. Tang and J. MacLennan, Wiley, 2005. , ISBN: 0-471-46261-6 (Reference)
• Various online materials

Student Conduct Code

Please review the academic conduct code

Tentative Course Schedule