CSCI 6905 Syllabus

The goal of this graduate course is to explore data mining concepts and techniques and the state-of-the-art of data mining research.

Students are expected to attend all the lectures and complete homeworks and a project. Each student will work individually on his/her project. The project can be implemenation-oriented or research-oriented. Each student needs to submit a project proposal to be discussed and approved by the instructor within the first 4 weeks, i.e., by Sept. 23. Each student will also be asked to report the progress of the project to the instructor by Nov. 18. By the end of the semester, students are required to give an oral presentation to the entire class and submit a project report. Students are encouraged to discuss their projects with the instructor at any stage. Bonus points may be given to excellent projects. Grades will be based on the homeworks, project presentation & report, the midterm and final exam. The final exam will be a accumulative one.

Class work will be counted as follows.

Homeworks - 20%
Midterm exam (Oct. 14) - 20%
Final exam (Dec. 11) - 30%
Project presentation (Dec. 2 & Dec. 7) - 10%
Project report (due Dec. 7) - 20%

The following table gives the percentage of points needed for the associated final grade:

A    90-100%
B    80-89%
C    70-79%
F    below 70%

Outline	Reading
I. Introduction to data mining	Chap 1 and related papers
II. Data preprocessing	Chap 2
III. Association rule mining	Chap 5 and related papers
IV. Classification	Chap 6 and related papers
Midterm exam	Wednesday, Oct. 14
V. Clustering	Chap 7 and related papers
VI. Data warehousing	Chap 3 & 4
VII. Data mining on time-series and sequence data, spatial data mining, text mining, and web mining	Chap 8 & 10 and related papers
VIII. Data mining applications and trends	Chap 11
Project presentations	Dec. 2 & Dec. 7
Final exam	Friday, Dec. 11, 11:00am-1:30pm