cs/cmpe 636 – advanced data mining

CS/CMPE 636 – Advanced Data Mining

Outline

CS 636 - Adv. Data Mining (Wi 2004/2005) - Asim Karim @ LUMS 2

Description

Cover recent developments in some key areas of data mining: Mining data streams Cluster analysis Web mining

Prepare students for research work in data mining. Follow a lecture-discussion format 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.


Goals

To expose key research areas in data mining To develop article comprehension and critical review

skills To improve research and presentation quality for

possible publication


After Taking this Course…

You should be able to … comprehend and critically analyze data mining research design and implement data mining solutions write and publish articles


Prerequisites

CS 536 – Data Mining: This course provides necessary concepts and foundations for CS 636

Permission of instructor For those who have taken CS 535 (Machine Learning) and

are motivated and willing to learn data mining basics on their own

For any other super motivated person

Passion for learning, research, and development


Grading

Points distribution

Project 35%

Quizzes 20%

Assignments 5%

Attendance and CP5%

Exam 35%


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 do and 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.


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.

Classroom behavior Maintain classroom sanctity by remaining quiet and attentive If you have a need to talk and gossip, please leave the

classroom so as not to disturb others Dozing is allowed provided you do not snore load


Project

Research, design, implement and evaluate a data mining algorithm

You may choose a problem of your liking within the focus areas of this course (after consultation with me) or select one suggested by me

Each of you must do the project independently Overview

Literature search and annotated bibliography Research review Solution/algorithm design Implementation and evaluation Report and presentation

Start thinking about the project now


Summarized Course Contents

Review Mining data streams

Data stream models Algorithms Intrusion detection

Cluster analysis Similarity measures Algorithms for data streams and mixed-type datasets

Web mining Intelligent information retrieval Newgroup mining

Coverge and contents may vary according to the dynamics of the course


Course Material

Required No required textbook Set of articles to be put in the course folder on COMMON

drive

Supplementary material Data Mining: Introductory and Advanced Topics, Dunham,

Pearson Education, 2003. Data Mining: Concepts and Techniques, Han and Kamber,

Morgan Kaufmann, 2001.

Other resources Books in library Web


Course Web Site

For announcements, lecture slides, handouts, assignments, quiz solutions, web resources:

http://suraj.lums.edu.pk/~cs636w04/

The resource page has links to information available on the Web. It is basically a meta-list for finding further information.


Other Stuff

How to contact me? Office hours: 10.00 to 12.00 MW (office: 429) E-mail: [email protected] By appointment: e-mail me for an appointment before

coming

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.


General Reference Books in LUMS Library (1)

Data Mining: Concepts, Models, Methods, and Algorithms, Mehmed Kantardzic, 006.3 K167D, 2003.

Principles of Data Mining, Hand and Mannila, 006.3 H236P, 2001. The elements of statistical learning; data mining, inference, and prediction,

Tervor Hastie, Robert Tibshirani and Jerome Friedman, 006.31 H356E 2001. Data mining and uncertain reasoning;an integrated approach, Zhengxin Chen,

006.321 C518D 2001. Graphical models; methods for data analysis and mining, Christian Borgelt

and Rudolf Kruse, 006.3 B732G 2001. Information visualization in data mining and knowledge discovery, Usama

Fayyad (ed.), 006.3 I434 2002. Intelligent data warehousing;from data preparation to data mining, Zhengxin

Chen, 005.74 C518I 2002. Machine learning and data mining;methods and applications, Michalski,

Ryszard S., ed.;Bratko, Ivan, ed.;Kubat, Miroslav, ed., 006.31 M149 1999. Data Mining: Practical Machine Learning Tools and Techniques with Java

Implementations, Witten et al., Morgan Kaufmann, 006.3 W829D, 2000.


General Reference Books in LUMS Library (2)

Machine Learning, Tom Mitchells, McGraw-Hill, 1997.

Managing and mining multimedia databases, Bhavani Thuraisingbam, 006.7 T536M 2001.

Mastering data mining;the art and science of customer relationship management, J.A. Michael Berry and Gordon Linoff, 006.3 B534M 2000.

Data mining explained;a manager's guide to customer-centric business intelligence, Rhonda Delmater and Monte Hancock, 006.3 D359D 2001.

Data mining solutions;methods and tools for solving real-world problems, Christopher Westphal and Teresa Blaxton, 006.3 W537D 1998.

cs/cmpe 636 – advanced data mining

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