Download CS636 - Advanced Data Mining

CS 636 – Advanced Data Mining
Instructor’s Name:
Asim Karim
Year:
Office No. & Email:
429, [email protected]
Quarter: Winter
Office Hours:
TBA
Category: MS/PhD
TA for the Course:
TBA
Course Code
(Units)
Course
Description
2004-05
CS 636 – Adv. Data Mining
(3 Units)
This course will cover recent developments in some key areas of data mining preparing students
for research work in these areas. A lecture-discussion format will be followed where topics are
introduced and techniques critically discussed. The majority of the material discussed will be
derived from research publications. Students will be expected to read before coming to class and
participate in the discussions. Emphasis will be placed on the design and implementation of
efficient and scalable algorithms for data mining. The course project will require students to
research, design, implement, and present their solution to a data mining problem.
Core/Elective
Elective. Strongly recommended for those who want to pursue research in data mining.
Pre-requisites
CS 536 Data Mining, or permission of instructor.
.
Goals
TextBooks,
Programming
Environment,
etc.
1.
2.
3.
Expose key research areas in data mining
Develop article comprehension and critical review skills
Improve research and presentation quality for possible publication
Required Materials:
Core set of research articles
Reference Texts:
1. Data Mining: Introductory and Advanced Topics, M.H. Dunham, Pearson Education, 2003.
2. Data Mining: Concepts and Techniques, Jiawei Han and Micheline Kamber, Morgan Kaufmann
Publishers, 2001.
CS 636 – Advanced Data Mining
Year:
2004-05
Quarter: Winter
Lectures,
Tutorials &
Attendance
Policy
Grading
Additional
Details
There will be 19 sessions (lectures-discussions) of 75 minutes each, and one in-class midterm
exam.
Attendance is essential, and attendance and class participation will be evaluated.
10%
35%
35%
5%
15%
Assignments
Project (multiple sub-instruments and submissions)
Midterm Exam (8th week)
Attendance and class participation
Quizzes
The course website will be the primary source for announcements and reading material including
lecture slides, handouts, and web links. http://suraj.lums.edu.pk/~cs636w04
Cheating and plagiarism will not be tolerated and will be referred to the disciplinary committee for
appropriate action. Students may discuss with others; however, it is required that solutions are
written independently. Downloading code segments from the internet and presenting them as your
own work is considered plagiarism.
CS 636 – Advanced Data Mining
Year:
Topics
2004-05
Quarter: Winter
Sessions
1-2
1.
Introduction / Review
2.
Mining data streams
Data stream models; Time and space efficient algorithms;
approximate algorithms; Intrusion detection models
2-8
3.
Clustering
Similarity measures; Clustering mixed numeric and categorical
attribute datasets; Outlier detection
9-13
4.
Web mining
Intelligent information retrieval; Mining newsgroups; Web usage
mining
14-17
19-20
19-20