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CSE/CBS 572 Data Mining

CSE/CBS 572 Data Mining. Huan Liu, CSE, CEAS, ASU http://www.public.asu.edu/~huanliu/DM07S/cse572.html. CSE 572. Contents of basic and advanced topics Classification, Clustering, Association, and Applications

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CSE/CBS 572 Data Mining

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  1. CSE/CBS 572 Data Mining Huan Liu, CSE, CEAS, ASU http://www.public.asu.edu/~huanliu/DM07S/cse572.html CSE/CBS 572: Data Mining by Huan Liu

  2. CSE 572 • Contents of basic and advanced topics Classification, Clustering, Association, and Applications • Format – An interactive and hands-on course with ample opportunities to work, create and share Paper reading, discussion, project, presentation, or any learning activities you can suggest • Assessment in various ways Class participation, assignments, quizzes, a course project, presentations, 1 or 2 exams CSE/CBS 572: Data Mining by Huan Liu

  3. You: our future data mining star, a potential zillionaire • TA: Lei Tang, l.tang@asu.edu • Me: Huan Liu, huanliu@asu.edu • Where: Brickyard 566 • When: see on the course website, or by appointment • “No pain, no gain”, or “As you sow, so you shall reap”. We will also learn the principle of “No Free Lunch”. • MyASU will be used, make sure won’t miss important announcement and your email address stays current (I push some information to you via emails sometimes) CSE/CBS 572: Data Mining by Huan Liu

  4. Course Format • What is the effective teaching of graduate data mining ? Your feedback is keenly sought. • Current research papers - the main categories to be found on the course web site. • You can choose one of the textbooks listed. It is an entering point for you to access related subjects. The truth is It is a fast changing field. • Everyone is expected to read research papers and participate in class discussion. • Paper presentations. • Project presentations. • Presentations will also be evaluated in class. CSE/CBS 572: Data Mining by Huan Liu

  5. Point distribution (tentative) • Projects (20-25%) • Reading/presentation assignment (5%) • Exam(s) (50%) • Assignments (20-25%), and class participation, quizzes (up to 10% extra credit) • Late penalty, YES, increasing exponentially. • Academic integrity(http://www.public.asu.edu/~huanliu/conduct.html) CSE/CBS 572: Data Mining by Huan Liu

  6. Research paper reading • We will provide a reading list and you can also choose your favorite • All are expected to search for and read the selected papers – part of the learning process. • What is it about (e.g., key idea, basic algorithm)? • What are points to discuss and improve? • What can we do with it? • What to submit? (see more on the class website) • A brief report that describes the above and 2 questions suitable for quizzes/tests with solutions • A set of presentation slides for 20 minutes • Due date: TBA, use digital drop box • Grading criteria include (1) quality of additional papers you select, (2) slides for presentation, (3) the report, and (4) oral presentation will be selected among the best submissions and presenters will be given extra credit based on presentation • Presentation can start as early as in Feburary, if possible. CSE/CBS 572: Data Mining by Huan Liu

  7. Project • Proposal • A theme-based project for all in class? • More discussion later • Proposal presentation, discussion, revision • A project worth the effort of a semester’s work • Progress report • Final report • Class presentation and/or demo • One key goal of this course is to take advantage of your intelligence and (limited) experience (so you’re bold and creative) to expand your knowledge in creating something useful and interesting CSE/CBS 572: Data Mining by Huan Liu

  8. Topic Distribution (tentative) CSE/CBS 572: Data Mining by Huan Liu

  9. Categories of interests (including design and implementation) • Data and application security • Data mining and privacy • Data reduction and selection • Streaming data reduction • Dealing with large data (column- & row-wise) • Search bias, overfitting • Learning algorithms • Ensemble methods • Semi-supervised learning • Active learning and co-training • Bioinformatics or others • A discussion board will be created, if needed CSE/CBS 572: Data Mining by Huan Liu

  10. Your first assignment is to think • Think about what you want to accomplish in this course. • List 2 of your areas of interests (don’t be restricted by the previous list, and this is one of some rare opportunities that allow you to day-dream and earn grade points), and briefly justify your choices. • Pick an area of interests and choose a general topic for paper presentation. • Submission is in hardcopy • Complete the above and submit it in the 3rd class (Monday, January 29, 2007). CSE/CBS 572: Data Mining by Huan Liu

  11. Your 2nd homework (project related) assignment • Find some (2-3) Web 2.0 or HealthCare applications • Wikipedia, some examples: facebook, del.icio.us,myspace, digg, … • Massive data of various types in a hospital environment • Surf the Web and compile their URLs and functions • Choose your favorite one and explain why • How can data mining help in your perspective with your limited knowledge of data mining • Test-drive it (manually, imagine that you have what you wish for, …) • Write a summary about your experience, challenges you encountered, and suggestions if any • Due on Wednesday February 7, 2007 CSE/CBS 572: Data Mining by Huan Liu

  12. Selecting a paper of your interest • First, continue from your 1st (and/or 2nd) homework assignments about your category of interest • Second, you may form presentation groups (e.g., 2-3 a group for discussion purposes) • Third, each student picks a paper from the given category and find 1 additional high-quality relevant paper • Submit it through myASU • The first student in each category will present the given paper (s/he does not need to look for another paper) • TA will organize and help you and compile a list of all papers at the end • Write a summary for your selected paper including • What is it about • Why is it significant and relevant • Where is it published and when CSE/CBS 572: Data Mining by Huan Liu

  13. Introduction • The need for data mining in the Internet era • Data everywhere, examples? • Data mining • Text mining • Image mining • Web mining (log, link, content, blog) • Bioinformatics • Many products and abundant applications • Where do we stand CSE/CBS 572: Data Mining by Huan Liu

  14. What is data mining • Data mining is • extraction of useful patterns from data sources, e.g., databases, texts, web, image. • the analysis of (often large) observational data sets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data owner. CSE/CBS 572: Data Mining by Huan Liu

  15. Patterns (1) • Patterns are the relationships and summaries derived through a data mining exercise. • Patterns must be: • valid • novel • potentially useful, or actionable • understandable CSE/CBS 572: Data Mining by Huan Liu

  16. Patterns (2) • Patterns are used for • prediction or classification • describing the existing data • segmenting the data (e.g., the market) • profiling the data (e.g., your customers) • Detection (e.g., intrusion, fault, anomaly) CSE/CBS 572: Data Mining by Huan Liu

  17. Data (1) • Data mining typically deals with data that have already been collected for some purpose other than data mining. • Data miners usually have no influence on data collection strategies. • Large bodies of data cause new problems: representation, storage, retrieval, analysis, ... CSE/CBS 572: Data Mining by Huan Liu

  18. Data (2) • Even with a very large data set, we are usually faced with just a sample from the population. • Data exist in many types (continuous, nominal) and forms (credit card usage records, supermarket transactions, government statistics, text, images, medical records, human genome databases, molecular databases). CSE/CBS 572: Data Mining by Huan Liu

  19. Typical DM tasks • Classification: • mining patterns that can classify future data into known classes. • Association rule mining: • mining any rule of the form X Y, where X and Y are sets of data items. • Clustering: • identifying a set of similar groups in the data CSE/CBS 572: Data Mining by Huan Liu

  20. Sequential pattern mining: A sequential rule: A B, says that event A will be immediately followed by event B with a certain confidence • Deviation/anomaly/exception detection: discovering the most significant changes in data • Data visualization (or visual analytics): using graphical methods to show patterns in data. • High performance computing • Bioinformatics CSE/CBS 572: Data Mining by Huan Liu

  21. Why data mining • Rapid computerization of businesses produces huge amounts of data • How to make best use of data to our advantages? • A growing realization: knowledge discovered from data can be used for competitive advantage and to increase business intelligence. • There are problems that might not be suitable for data mining – Top 10 Statistics Problems for CapitalOne (Bill Khan’s invited talk at SIGKDD’06) CSE/CBS 572: Data Mining by Huan Liu

  22. Make use/sense of your data assets • Many interesting things you want to find cannot be found using database queries “find me people likely to buy my products” “Who are likely to respond to my promotion” • Fast identify underlying relationships and respond to emerging opportunities CSE/CBS 572: Data Mining by Huan Liu

  23. Why now and for the near future • The data is abundant. • The data is being collected or warehoused. • The computing power is affordable. • The competitive pressure is increasing. • Data mining tools have become available. • New challenges • New data types evolve • New applications emerge CSE/CBS 572: Data Mining by Huan Liu

  24. DM fields • Data mining is an emerging multi-disciplinary field: Statistics Machine learning Databases Visualization OLAP and data warehousing High-performance computing ... CSE/CBS 572: Data Mining by Huan Liu

  25. Summary • What is data mining? KDD - knowledge discovery in databases: non-trivial extraction of implicit, previously unknown and potentially useful/actionable information • Why do we need data mining? Wide use of computer systems - data explosion - knowledge is power – but we’re data rich, knowledge poor – useful, understandable and actionable knowledge ... • Data mining is not a plug-and-play, so we are not done yet and need to continue this class … CSE/CBS 572: Data Mining by Huan Liu

  26. An Overview of KDD Process (Guess which is which) CSE/CBS 572: Data Mining by Huan Liu

  27. Web mining – an application • The Web is a massive database • Semi-structured data • XML and RDF • Web mining • Content • Structure • Usage • Link analysis CSE/CBS 572: Data Mining by Huan Liu

  28. About project • Data collection • How, where, what to collect • Evaluation methods • How and why • Analysis and mining • What requires our (yet-to-develop) expertise • For what • Potentially useful methods • For example, graph theories, social networks • Great applications CSE/CBS 572: Data Mining by Huan Liu

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