CIS 453/553, Data Mining

CIS453/553, Winter 2008

Data Mining

[ Assignments | CIS | News]

Course Description: Data Mining is a multidisciplinary field which involves databases, machine learning, statistics, knowledge acquisition, information retrieval, high-performance computing, and data visualization. Data Mining emerged as an interesting research topic since 1980s and has made great strides during the 1990s. It also has got many successful applications in industry. In this course, we will study methods for data management and preprocessing, concept description, association rule mining, classification and prediction, data cluster analysis and outlier analysis. We will view data mining as the discovery of interesting patterns in data that can be used to describe or summarize the data and to predict future instances of the data. Such patterns are considered to be knowledge discovered by the data mining process. Students will be guided to apply or build different mining tools by themselves to analyze real data sets, such as biomedical data, network data, financial data and retail data.
Prerequisites: Database related course (e.g., CIS 451 or CIS 399) recommended
Time and Place: Wednesdays and Fridays at 2:00pm - 3:20pm in 200 Deschutes Hall.(room changed!)
Instructor: Dejing Dou, 303 Deschutes, phone 346-4572, email dou@cs.uoregon.edu.
GTF: TBA
Office hours:
- Dejing Fridays 3:30pm - 4:30pm or by appointment (I like to talk with students)
News: Keep an eye on the course news for miscellaneous announcements.
Evaluation:
- The homework will be a combination of written and programming work.
- The Final Project will be due in parts: Part 1 (project proposal) is due Friday February 22 and Part 2 (final report) on Friday March 21. We assume that you will work on it individually. If you wish to work in a group, please check with the instructor.
The approximate weighting of the work done will be

Homework Assignments 40%

Midterm 30%

Final Project 30%
Texts:
- (Required) Jiawei Han and Micheline Kamber Data Mining: Concepts and Techniques, Second Edition, Morgan Kaufmann. (see text website.)
Supplemental Reading:
- I. Witten and E. Frank Data Mining: Practical Machine Learning Techniques with Java Implementations, Morgan-Kaufmann, 2000. (Good for the final project)

Approximate Syllabus and Lecture Slides

Week of	Chapter	Description and Slides	Events

Jan 7	1	intro, issues in Data Mining
Jan 14	2	data preprocessing, data reduction
Jan 21	3	data warehouse, data cube, OLAP, OLAM and DMQL
Jan 28	4, 5	Concept Description, Association Mining
Feb 4	5,6	Association Mining (cont'd), Classification
Feb 11	6	Classification and Prediction
Feb 18	7	Clustering and Outlier Analysis	Final Project Part 1 (project proposal): Monday, Feb 25
Feb 25		Midterm Review	Midterm Friday, Feb 29
Mar 3	8,9,10	Distributed, Complex Types, Graph, social network
Mar 10		Project implementations by students (no lectures)
Mar 17		Project Presentations Wednesday, Mar 19, 3:15pm - 5:15pm ;	Final Project Part 2(final report): Friday, Mar 21

Late policy: All assignments are due by 11:59pm of the stated due date, though it is much safer to turn them in at class time. Late papers are subject to a 10% per day penalty. Late assignments will not be accepted once the graded papers are returned or once sample solutions are handed out. It is your responsibility to turn in the assignments on time. Make sure that electronically submitted assignments are actually delivered.
Some useful links:
- Data Mining Research Links collected by Prof. Mohammed J. Zaki in RPI.
- Weka data mining software.
- RuleQuest research data mining tools.

Homework Assignments	40%
Midterm	30%
Final Project	30%