Download Course outline

CS/CMPE 636 –
Advanced Data Mining
Outline
Description
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Cover recent developments in some key areas of data mining:
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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.
CS 636 - Adv. Data Mining (Wi 2004/2005) - Asim Karim @ LUMS
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Goals
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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
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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
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Prerequisites
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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
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Passion for learning, research, and development
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Grading
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Points distribution
Project
Quizzes
Assignments
Attendance and CP
Exam
35%
20%
5%
5%
35%
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Policies (1)
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Quizzes
 Most
quizzes will be announced a day or two in advance
 Unannounced quizzes are also possible
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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
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Plagiarism
 Do
NOT pass someone else’s work as yours! Write in your
words and cite the reference. This applies to code as well.
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Policies (2)
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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.
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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 
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Project
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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
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Literature search and annotated bibliography
Research review
Solution/algorithm design
Implementation and evaluation
Report and presentation
Start thinking about the project now
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Summarized Course Contents
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Review
Mining data streams
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Cluster analysis
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Similarity measures
Algorithms for data streams and mixed-type datasets
Web mining
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Data stream models
Algorithms
Intrusion detection
Intelligent information retrieval
Newgroup mining
Coverge and contents may vary according to the dynamics of the
course
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Course Material
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Required
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No required textbook
 Set of articles to be put in the course folder on COMMON
drive
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Supplementary material
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Data Mining: Introductory and Advanced Topics, Dunham,
Pearson Education, 2003.
Data Mining: Concepts and Techniques, Han and Kamber,
Morgan Kaufmann, 2001.
Other resources
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Books in library
Web
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Course Web Site
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For announcements, lecture slides, handouts,
assignments, quiz solutions, web resources:
http://suraj.lums.edu.pk/~cs636w04/
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The resource page has links to information available on
the Web. It is basically a meta-list for finding further
information.
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Other Stuff
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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
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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.
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General Reference Books in LUMS Library (1)
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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.
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General Reference Books in LUMS Library (2)
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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 customercentric 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.
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