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CS/CMPE 636 – Advanced Data Mining

CS/CMPE 636 – Advanced Data Mining. Outline. 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.

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CS/CMPE 636 – Advanced Data Mining

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  1. CS/CMPE 636 – Advanced Data Mining Outline

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

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

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

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

  6. Grading • Points distribution Project 35% Quizzes 20% Assignments 5% Attendance and CP 5% Exam 35% CS 636 - Adv. Data Mining (Wi 2004/2005) - Asim Karim @ LUMS

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

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

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

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

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

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

  13. Other Stuff • How to contact me? • Office hours: 10.00 to 12.00 MW (office: 429) • E-mail: akarim@lums.edu.pk • 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. CS 636 - Adv. Data Mining (Wi 2004/2005) - Asim Karim @ LUMS

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

  15. 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 636 - Adv. Data Mining (Wi 2004/2005) - Asim Karim @ LUMS

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