1. CS 536 –Data Mining
Outline

2. CS 536 - Data Mining (Au 2003/2004) - Asim Karim @ LUMS
Description
A comprehensive introduction to the concepts and techniques in data mining
data mining process – its need and motivation
data mining tasks and functionalities
role of data warehousing
association rule mining
classification and prediction
clustering
text and web mining
mining sequential data
evaluation of DM tools and programming of algorithms in C/C++/Java
Emphasis on concept building, algorithm evaluation and applications
CS Data Mining (Au 2003/2004) - Asim LUMS
CS/CMPE Neural Networks (Sp 2002/2003) - Asim LUMS

3. CS 536 - Data Mining (Au 2003/2004) - Asim Karim @ LUMS
Goals
To provide a comprehensive introduction to data mining
To develop conceptual and theoretical understanding of the data mining process
To provide hands-on experience in the implementation and evaluation of data mining algorithms and tools
To develop interest in data mining research
CS Data Mining (Au 2003/2004) - Asim LUMS

4. After Taking this Course…
You should be able to …
understand the need and motivation for data mining
understand the characteristics of different data mining tasks
decide what data mining task and algorithm to use for a given problem/data set
implement and evaluate data mining solutions
use commercially available DM tools
CS Data Mining (Au 2003/2004) - Asim LUMS

5. Before Taking This Course…
You should be comfortable with…
Data structures and algorithms!
CS213 is a prerequisite
You should be comfortable with algorithm descriptions and implementations in a high-level programming language
Databases
Understanding of the database concept and familiarity with database terms and terminology
CS341 is recommended, not required
Basic math background
Algebra, calculus, etc
Programming in a high-level language
C/C++ or Java
CS Data Mining (Au 2003/2004) - Asim LUMS

6. CS 536 - Data Mining (Au 2003/2004) - Asim Karim @ LUMS
Grading
Points distribution
Quizzes (5 to 6) %
Assignments (hand + computer) 20%
Project %
Midterm exam %
Final exam (comprehensive) 30%
CS Data Mining (Au 2003/2004) - Asim LUMS

7. CS 536 - Data Mining (Au 2003/2004) - Asim Karim @ LUMS
Policies (1)
Quizzes
Most quizzes will be announced a day or two in advance
Unannounced quizzes are also possible
Sharing
No copying is allowed for assignments. Discussions are encouraged; however, you must submit your own work
Violators can face mark reduction and/or reported to Disciplinary Committee
Plagiarism
Do NOT pass someone else’s work as yours! Write in your words and cite the reference. This applies to code as well.
CS Data Mining (Au 2003/2004) - Asim LUMS

8. CS 536 - Data Mining (Au 2003/2004) - Asim Karim @ LUMS
Policies (2)
Submission policy
Submissions are due at the day and time specified
Late penalties: 1 day = 10%; 2 day late = 20%; not accepted after 2 days
An extension will be granted only if there is a need and when requested several days in advance.
CS Data Mining (Au 2003/2004) - Asim LUMS

9. CS 536 - Data Mining (Au 2003/2004) - Asim Karim @ LUMS
Project
Design, implementation and evaluation of a data mining application
You may choose a problem of your liking (after consultation with me) or select one suggested by me
You may do the project in groups (of 2)
Start thinking about the project now
CS Data Mining (Au 2003/2004) - Asim LUMS

10. Summarized Course Contents
Introduction and motivation
The data mining process – tasks and functionalities
Intro to data warehousing and its role in data mining
Data preprocessing for data mining – data cleaning, reduction, summarization, normalization, etc
Mining association rules – algorithms and applications
Mining through classification and prediction – algorithms and applications
Mining by clustering – algorithms and applications
Mining text and web data
Mining sequential data
CS Data Mining (Au 2003/2004) - Asim LUMS

11. CS 536 - Data Mining (Au 2003/2004) - Asim Karim @ LUMS
Course Material
Required textbook
Data Mining: Concepts and Techniques, Han and Kamber, 2001
Supplementary material
Data Mining: Concepts, Models, Methods, and Algorithms, Mehmed Kantardzic, 2003
Principles of Data Mining, Hand, Mannila, and Smyth, 2001
Handouts (as and when necessary)
Other resources
Books in library
Web
CS Data Mining (Au 2003/2004) - Asim LUMS

12. CS 536 - Data Mining (Au 2003/2004) - Asim Karim @ LUMS
Course Web Site
For announcements, lecture slides, handouts, assignments, quiz solutions, web resources:
The resource page has links to information available on the Web. It is basically a meta-list for finding further information.
CS Data Mining (Au 2003/2004) - Asim LUMS

13. CS 536 - Data Mining (Au 2003/2004) - Asim Karim @ LUMS
Other Stuff
How to contact me?
Office hours: 2.30 to 4.00 PM MW (office: 132)
Stop by my office
By appointment
Philosophy
Knowledge cannot be taught; it is learned.
Be excited. That is the best way to learn. I cannot teach everything in class. Develop an inquisitive mind, ask questions, and go beyond what is required.
I don’t believe in strict grading. But… there has to be a way of rewarding performance.
CS Data Mining (Au 2003/2004) - Asim LUMS

14. Reference Books in LUMS Library (1)
The elements of statistical learning; data mining, inference, and prediction, Tervor Hastie, Robert Tibshirani and Jerome Friedman, H356E 2001.
Data mining and uncertain reasoning;an integrated approach, Zhengxin Chen, C518D 2001.
Graphical models; methods for data analysis and mining, Christian Borgelt and Rudolf Kruse, B732G 2001.
Information visualization in data mining and knowledge discovery, Usama Fayyad (ed.), I
Intelligent data warehousing;from data preparation to data mining, Zhengxin Chen, C518I 2002.
Machine learning and data mining;methods and applications, Michalski, Ryszard S., ed.;Bratko, Ivan, ed.;Kubat, Miroslav, ed., M
CS Data Mining (Au 2003/2004) - Asim LUMS

15. Reference Books in LUMS Library (2)
Managing and mining multimedia databases, Bhavani Thuraisingbam, T536M 2001.
Mastering data mining;the art and science of customer relationship management, J.A. Michael Berry and Gordon Linoff, B534M 2000.
Data mining explained;a manager's guide to customer-centric business intelligence, Rhonda Delmater and Monte Hancock, D359D 2001.
Data mining solutions;methods and tools for solving real-world problems, Christopher Westphal and Teresa Blaxton, W537D 1998.
CS Data Mining (Au 2003/2004) - Asim LUMS