SYLLABUS

CS455/555, Fall, 2009

Data Mining

Instructor: Henry Tzeng/RB374

Classes: 10-10:50, MWF, RB104

Office hours: 9:00-10:00, MWF, RB374

Text Book

Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques, Second Edition, Morgan Kaufman, 2006.

Main Contents

Topics include

Introduction

Data Preprocessing

Data Warehouse

Advanced Data Cube Technology and Data Generalization

Mining Frequent Patterns, Association and Correlations

Classification and Prediction

Cluster Analysis

Prerequisites: CS 324, 335 (knowledge of database systems is helpful.)

Class Projects (40 % of final grade)

Several projects will be given in the class, (Penalty of delay will be 5% for each day.)

Independent Project (30 % of final grade): Each student needs to choose and study a topic related to Data Mining. At the end of the semester, each student will give presentation in the class (30-50 minutes).

Tests: at least one test (30 % of the final grade).

Final grade: the letter grade will depend on the following standard (plus and minus will be adjusted accordingly).

A B C

90% 80% 70%

Class attendance: Attendance in each class is required. (Penalty of absences will be up to 10% of final grade.)

Access and Opportunity:

If you need course adaptations or accommodations because of a disability, if you have emergency medical information to share with me, or if you need special arrangements in case the building must be evacuated, please make an appointment with me as soon as possible.

Student Academic Ethics Policy:

Academic dishonesty by a student will not be tolerated.

http://www.bsu.edu/sa/article/0,,34919--,00.html