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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