CS/CMPE 636 – Advanced Data Mining Outline

CS Adv. Data Mining (Wi ) - Asim LUMS2 Description Cover recent developments in some key areas of data mining:  Mining data streams  Semi-supervised learning  Text mining Prepare students for research work in data mining and machine learing. Follow a lecture-discussion format where topics are introduced and techniques critically discussed.  Students to lead discussions on key papers in the topic area. 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 Adv. Data Mining (Wi ) - Asim LUMS3 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 Adv. Data Mining (Wi ) - Asim LUMS4 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 Adv. Data Mining (Wi ) - Asim LUMS5 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 Adv. Data Mining (Wi ) - Asim LUMS6 Grading Points distribution Project 40% Presentation15% Quizzes + HW10% Attendance + CP5% Exam30%

CS Adv. Data Mining (Wi ) - Asim LUMS7 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 Adv. Data Mining (Wi ) - Asim LUMS8 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 Adv. Data Mining (Wi ) - Asim LUMS9 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 Adv. Data Mining (Wi ) - Asim LUMS10 Summarized Course Contents Review Mining data streams  Data stream models  Maintaining stats over data streams  Clustering and classifying data streams  Anomaly detection Semi-supervised learning  Introduction  Approaches and algorithms Text mining  Document understanding  Sentiment or opinion extraction  Mining web forums, chat rooms, SMS, P2P networks, personal information, etc Coverage and contents may vary according to the dynamics of the course

CS Adv. Data Mining (Wi ) - Asim LUMS11 Course Material Required  No required textbook  Set of articles to be put in the course folder on COMMON drive Supplementary material  Data Mining: Concepts and Techniques, 2 nd Edition, Han and Kamber, Morgan Kaufmann,  Introduction to Data Mining, Tan et al., Pearson Addison Wesley, Other resources  Books in library  Web

CS Adv. Data Mining (Wi ) - Asim LUMS12 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 Adv. Data Mining (Wi ) - Asim LUMS13 Other Stuff How to contact me?  Office hours: to MW (office: 429)   By appointment: 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 Adv. Data Mining (Wi ) - Asim LUMS14 General Reference Books in LUMS Library (1) Data Mining: Introductory and Advanced Topics, M. H. Dunham, Pearson Addison Wesley, Data Mining: Concepts, Models, Methods, and Algorithms, Mehmed Kantardzic, K167D, Principles of Data Mining, Hand and Mannila, H236P, The elements of statistical learning; data mining, inference, and prediction, Tervor Hastie, Robert Tibshirani and Jerome Friedman, H356E The elements of statistical learning; data mining, inference, and prediction Data mining and uncertain reasoning;an integrated approach, Zhengxin Chen, C518D Data mining and uncertain reasoning;an integrated approach Graphical models; methods for data analysis and mining, Christian Borgelt and Rudolf Kruse, B732G Graphical models; methods for data analysis and mining Information visualization in data mining and knowledge discovery, Usama Fayyad (ed.), I Information visualization in data mining and knowledge discovery Intelligent data warehousing;from data preparation to data mining, Zhengxin Chen, C518I Intelligent data warehousing;from data preparation to data mining Machine learning and data mining;methods and applications, Michalski, Ryszard S., ed.;Bratko, Ivan, ed.;Kubat, Miroslav, ed., M Machine learning and data mining;methods and applications Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Witten et al., Morgan Kaufmann, W829D, 2000.

CS Adv. Data Mining (Wi ) - Asim LUMS15 General Reference Books in LUMS Library (2) Machine Learning, Tom Mitchells, McGraw-Hill, Introduction to Machine Learning, E. Alpaydin, Prentice Hall, Managing and mining multimedia databases, Bhavani Thuraisingbam, T536M Managing and mining multimedia databases Mastering data mining;the art and science of customer relationship management, J.A. Michael Berry and Gordon Linoff, B534M Mastering data mining;the art and science of customer relationship management Data mining explained;a manager's guide to customer-centric business intelligence, Rhonda Delmater and Monte Hancock, D359D Data mining explained;a manager's guide to customer-centric business intelligence Data mining solutions;methods and tools for solving real-world problems, Christopher Westphal and Teresa Blaxton, W537D Data mining solutions;methods and tools for solving real-world problems