CSE/CBS 572: Data Mining
Spring 2008
|
Time: |
T,
Th: 1:40—2:55pm |
|
Place: |
BYAC 240 |
|
Web
Page: |
http://www.public.asu.edu/~jye02/CLASSES/Spring-2008/ |
Instructor:
Dr
Office: BY 568
Office hours: T, Th 3:00—4:30pm
Phone: 480-727-7451
Email: Jieping.Ye@asu.edu
TA:
Office: BY 584 AB
Office hours: T, Th 11am-12noon
Email: liang.sun.1@asu.edu
Course
Description
Recent
advances in technology along with the phenomenal growth of the Internet have
resulted in an explosion of data collected, stored, and disseminated by various
organizations. Because of its massive size, it is difficult for analysts to
sift through the data even though it may contain useful information. Data
mining holds great promise to address this problem by providing efficient
techniques to uncover useful information hidden in the large data repositories.
The
key objectives of this course are two-fold: (1) to teach the fundamental
concepts of data mining and (2) to provide extensive hands-on experience in applying
the concepts to real-world applications. The core topics to be covered in this
course include classification, association analysis, clustering, anomaly
detection, and semi-supervised clustering. At the end of this course, students
are expected to possess the fundamental skills needed to conduct research in
data mining or to apply data mining techniques to real-world applications.
Textbook
Introduction to Data Mining (2005)
By Pang-Ning Tan, Michael Steinbach, Vipin
Kumar
Addison Wesley
ISBN: 0-321-32136-7
Available at Amazon
Course Outline
1.
Introduction
- What is data mining?
2.
Data Preprocessing
·
Data sampling,
data cleaning, feature selection, and dimensionality reduction
3.
Classification
- Tree-based, rule-based,
and instance-based methods
- Bayesian methods (naive
Bayes and Bayesian belief networks)
- Neural networks, linear
discriminant analysis, support vector machines,
and ensemble methods
- Model evaluation
4.
Association Analysis
- Apriori algorithm and its
extensions
- Pattern evaluation
(subjective and objective interestingness measures)
- Sequential patterns and
graph mining
5.
Clustering
- Partitional and hierarchical
clustering methods
- Graph-based and
density-based methods
- Cluster evaluation
6.
Anomaly Detection
- Statistical-based and
density-based methods
7.
Advanced topic
- Semi-supervised
clustering
Grading
- Homework (6) 30%
- Project (2) 20%
- Exam (2) 40%
- Quiz (2) 10%
Class Project
TBA
Class Policy
Assignments
are due at the beginning of the lecture. Late assignments will not be
accepted. Attendance to lecture is
mandatory.
Academic
Integrity