1. CS 490/584 – Topics in AI: Data Mining (online)
Spring 2009 - 3 Credit Hours
Prerequisite: CS340
Time and place: 3:00 – 4:15 PM some Wednesdays
Online
Instructor: Xudong William Yu
Engineering Building 3039
(618) 650-2321
Email: xyu@siue.edu
URL: www.cs.siue.edu/xyu
Text Book: 1. “Data Mining: Concepts and Techniques”, by Jiawei Han and Micheline
Kamber, 2nd Edition, Morgan Kaufmann Publishers, August 2000.
2. “Data Mining: Practical Machine Learning Tools and Techniques with
Java Implementations”, by Ian Witten and Eibe Frank, 2nd edition, Morgan Kaufmann
Publishing, 2005.
Overview:
Data mining is the extraction of implicit, previously unknown, and potentially useful information from data.
The idea is to build computer programs that sift through databases automatically to identify regularities and
patterns. This course covers theoretical and practical aspects of current methods and selected systems for data
mining, knowledge discovery, and knowledge management. Topics include: fundamental concepts of data
mining and knowledge discovery, machine learning-based data mining methods such as classification,
clustering, summarization, regression, dependency modeling, and rule induction. Since this is an online course,
students will learn the course topics through a variety of methods such as reading assigned materials including
text, lecture notes, and research papers, engaging in discussion in the course forum, doing weekly homework,
and working in the Waikato Environment, which contains full, industrial-strength implementation of techniques
discussed in the text. Graduate students are expected also review assigned research papers and write critique
papers.
Electronic Communication:
The instructor will place class material (e.g., lecture notes, assignments, examples, announcements, etc) on the
network (Y) drive which you can also access via internet at www.cs.siue.edu/classes. Reading materials and
assignments will be posted and updated there weekly. The Computer Science Class Management System
(https://classes.cs.siue.edu) will be used for submission of homework and programs as well as for class
discussion. Students are expected to study all assigned reading material and do the assignments each week. A
face-to-face course organization meeting will be held 3:00 – 4:15 on Wednesday, January 14th in EB1012.
Programming Assignments:
There will be several (3 or 4) programming assignments using C++. There will also be several assignments
using the Waikato system. For programming assignments, a soft copy must be placed in your dropbox before
the due time. There will be a penalty of 10% per day late, including weekends.
Written Homework
There will be homework related to the reading material assigned for each chapter. They will be assignment
approximately once a week.
Topic Papers:

2. Each graduate student will be assigned two data mining articles from journal and conference proceedings and is
expected to write a 3-page summary AND critique for each article. Each paper will be evaluated on the basis
of thoroughness, coherence, and professionalism.
Tests:
There will be a mid-term and a comprehensive final examination. The mid-term will be held 3:00 – 4:15 PM
Wednesday, March 18th, 2009 in EB1012. A review session will be held at 3:00 – 4:15 on Wednesday March
4th 2009 in EB1012. The final exam date and time will be announced at a later time.
Undergraduate Student Grading:
Programming Assignments: 25%
Homework & Class Participation: 25%
Mid Term: 25%
Final examination: 25%
Graduate Student Grading:
Topic Papers 5%
Programming Assignments: 25%
Homework & Class Participation: 25%
Mid Term: 20%
Final examination: 25%
Grading Scale:
90 – 100 A
80 – 89 B
70 – 79 C
60 – 69 D
0 – 59 F