Kennesaw State University
DEPARTMENT OF COMPUTER SCIENCE AND INFORMATION SYSTEMS
CS8560: Data Mining
Instructor: Dr. Ying Xie
Office Number:
Office Hours:
Phone:
Email:
CL3019
(678) 797-2143
yxie2@kennesaw.edu
Course Description:
Due to the wide availability of huge amounts of data and the imminent need for turning data into
information and knowledge, data mining has attracted significant interest in recent years with its vast
domain of applications ranging from business analysis, scientific discovery, medical diagnosis, and
engineering design. This course covers major data mining concepts and techniques for uncovering
interesting data patterns hidden in large data sets, including data warehousing and OLAP technology,
association mining, classification and predication, clustering analysis, and time-series analysis.
Prerequisites:
CS 8530 Database Administration
Textbooks:
Data Mining: Concepts and Techniques, Second Edition (The Morgan Kaufmann Series in Data
Management Systems) by Micheline Kamber Jiawei Han
Learning Objectives:
Understand the following data warehousing techniques: Dimensional modeling, ETL, OLAP
Be able to design and implement a data warehouse.
Understand major data mining algorithms: including association mining, clustering, classification, and
time series analysis
Be able to implement data mining applications
Learning Outcomes:
1. Students will demonstrate skills on data warehouse design and implementation
2. Students will demonstrate skills on applying data mining algorithms and tools to extract various
patterns from different types of data
3 Students will demonstrate skills on data mining algorithm design
Assignments and Course Project
A series of assignments will be given for students to reinforce the concepts learned in class. Students
are also required to work on a course project that requires hand-on practices on the core data
warehousing and mining technologies.
Assessment and Grade Evaluation:
Assignments*
Course Project*
Midterm Test*
Final Test*
25%
25%
25%
25%
A
90% - 100%
B
80% - 89%
C
70% - 79%
D
60% - 69%
F
Below 60%
* Penalty for late submission applies to all assignments and tests
Course Topics (Tentative, subject to change):
Topic
Data mining introduction
Dimensional model
OLAP techniques
ETL technology
Association mining
Classification
Clustering
Time series analysis
Data mining applications
1 weeks
2 weeks
1 weeks
2 weeks
1.5 weeks
1.5 weeks
1.5 weeks
1.5 weeks
2 weeks
Course Materials
All course materials will be posted at http://csmoodle.kennesaw.edu. Please register to this website by
using your KSU student email (netid@students.kennesaw.edu)