CS/CMPE 536 –Data Mining Outline

CS Data Mining (Au ) - Asim LUMS2 Description A comprehensive introduction to the concepts and techniques in data mining  data mining process – its need and motivation  data mining tasks and functionalities  association mining  cluster mining  Web mining  text mining  evaluation of DM tools and programming of algorithms in C/C++/Java Emphasis on concept building, algorithm evaluation, and applications

CS Data Mining (Au ) - Asim LUMS3 Goals To provide a comprehensive introduction to data mining To develop conceptual and theoretical understanding of the data mining process To provide hands-on experience in the implementation and evaluation of data mining algorithms and tools To develop interest in data mining research

CS Data Mining (Au ) - Asim LUMS4 After Taking this Course… You should be able to … understand the need and motivation for data mining understand the characteristics of different data mining tasks decide what data mining task and algorithm to use for a given problem/data set implement and evaluate data mining solutions use commercially available DM tools

CS Data Mining (Au ) - Asim LUMS5 Before Taking This Course… You should be comfortable with… Data structures and algorithms!  CS213 is a prerequisite  You should be comfortable with algorithm descriptions and implementations in a high-level programming language Databases  Understanding of the database concept and familiarity with database terms and terminology  CS341 is recommended, not required Basic math background  Algebra, calculus, etc Programming in a high-level language  C/C++ or Java

CS Data Mining (Au ) - Asim LUMS6 Grading Points distribution Quizzes (~ 6)10% Assignments (hand + computer)15% Project 15% Midterm exam25% Final exam (comprehensive)35%

CS Data Mining (Au ) - 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 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 Data Mining (Au ) - 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 its need is established 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 loud

CS Data Mining (Au ) - Asim LUMS9 Project Design, implementation and evaluation of a data mining solution You may choose a problem of your liking (after consultation with me) or select one suggested by me You may do the project in groups (of 2) Start thinking about the project now

CS Data Mining (Au ) - Asim LUMS10 Summarized Course Contents Introduction and motivation The data mining process – tasks and functionalities Data preprocessing for data mining – data cleaning, reduction, summarization, normalization, etc Mining frequent patterns and associations – algorithms and applications Mining by clustering – algorithms and applications Mining Web data Intro to text mining

CS Data Mining (Au ) - Asim LUMS11 Course Material Required textbook  Data Mining: Concepts and Techniques, Han and Kamber, Second Edition, 2006 Supplementary material  Introduction to Data Mining, Tan et al., Addison-Wesley,  Web Data Mining, B. Liu, Sprinter,  Handouts (as and when necessary) Other resources  Books in library  Web (e.g. wikipedia)

CS Data Mining (Au ) - 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 Data Mining (Au ) - Asim LUMS13 Other Stuff How to contact me?  Office hours: to TR (office: 429)   By appointment: outside office hours 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 Data Mining (Au ) - Asim LUMS14 Reference Books in LUMS Library (1) Data Mining: Introductory and Advanced Topics, Dunham, Pearson Education, 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

CS Data Mining (Au ) - Asim LUMS15 Reference Books in LUMS Library (2) Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Witten et al., Morgan Kaufmann, W829D, 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