CS583 – Data Mining and Text MiningGeneral InformationCourse structureProgramming projectsGradingPrerequisitesTeaching materialsTopicsAny questions and suggestions?Rules and PoliciesIntroduction to Data MiningWhat is data mining?Example of discovered patternsMain data mining tasksMain data mining tasks (cont …)Why is data mining important?Why is data mining necessary?Why data mining now?Related fieldsData mining (KDD) processData mining applicationsWeb data extractionAlign and extract data items (e.g., region1)Opinion AnalysisFeature Based Analysis & SummarizationAn exampleVisual ComparisonCS 583 1CS583 – Data Mining and Text MiningCourse Web Pagehttp://www.cs.uic.edu/~liub/teach/cs583-fall-05/cs583.htmlCS 583 2General InformationInstructor: Bing Liu Email: [email protected] Tel: (312) 355 1318 Office: SEO 931 Course Call Number: 22887 Lecture times: 11:00am-12:15pm, Tuesday and Thursday Room: 319 SH Office hours: 2:00pm-3:30pm, Tuesday & Thursday (or by appointment)CS 583 3Course structureThe course has three parts: Lectures - Introduction to the main topicsProgramming projects2 programming assignments.To be demonstrated to meResearch paper reading A list of papers will be givenLecture slides will be made available at the course web pageCS 583 4Programming projectsTwo programming projectsTo be done individually by each studentYou will demonstrate your programs to me to show that they workYou will be given a sample datasetThe data to be used in the demo will be different from the sample dataCS 583 5GradingFinal Exam: 50% Midterm: 30% 1 midtermProgramming projects: 20% 2 programming assignments.Research paper reading (some questions from the papers will appear in the final exam).CS 583 6Prerequisites Knowledge of basic probability theory algorithmsCS 583 7Teaching materials Text Reading materials will be provided before the classReference texts: Data mining: Concepts and Techniques, by Jiawei Han and Micheline Kamber, Morgan Kaufmann, ISBN 1-55860-489-8. Principles of Data Mining, by David Hand, Heikki Mannila, Padhraic Smyth, The MIT Press, ISBN 0-262-08290-X. Introduction to Data Mining, by Pang-Ning Tan, Michael Steinbach, and Vipin Kumar, Pearson/Addison Wesley, ISBN 0-321-32136-7. Machine Learning, by Tom M. Mitchell, McGraw-Hill, ISBN 0-07-042807-7 Modern Information Retrieval, by Ricardo Baeza-Yates and Berthier Ribeiro-Neto, Addison Wesley, ISBN 0-201-39829-X Data mining resource site: KDnuggets DirectoryCS 583 8TopicsIntroductionData pre-processingAssociation rule mining Classification (supervised learning) Clustering (unsupervised learning) Post-processing of data mining resultsText mining Partial/Semi-supervised learningIntroduction to Web miningCS 583 9Any questions and suggestions?Your feedback is most welcome!I need it to adapt the course to your needs.Share your questions and concerns with the class – very likely others may have the same.No pain no gain – no magicThe more you put in, the more you getYour grades are proportional to your efforts.CS 583 10Rules and Policies Statute of limitations: No grading questions or complaints, no matter how justified, will be listened to one week after the item in question has been returned. Cheating: Cheating will not be tolerated. All work you submitted must be entirely your own. Any suspicious similarities between students' work will be recorded and brought to the attention of the Dean. The MINIMUM penalty for any student found cheating will be to receive a 0 for the item in question, and dropping your final course grade one letter. The MAXIMUM penalty will be expulsion from the University. Late assignments: Late assignments will not, in general, be accepted. They will never be accepted if the student has not made special arrangements with me at least one day before the assignment is due. If a late assignment is accepted it is subject to a reduction in score as a late penalty.CS 583 11Introduction to Data MiningCS 583 12What is data mining?Data mining is also called knowledge discovery and data mining (KDD)Data mining isextraction of useful patterns from data sources, e.g., databases, texts, web, image. Patterns must be:valid, novel, potentially useful, understandableCS 583 13Example of discovered patternsAssociation rules:“80% of customers who buy cheese and milk also buy bread, and 5% of customers buy all of them together”Cheese, Milk Bread [sup =5%, confid=80%]CS 583 14Main data mining tasksClassification:mining patterns that can classify future data into known classes. Association rule miningmining any rule of the form X   Y, where X and Y are sets of data items. Clusteringidentifying a set of similarity groups in the dataCS 583 15Main data mining tasks (cont …)Sequential pattern mining:A sequential rule: A B, says that event A will be immediately followed by event B with a certain confidenceDeviation detection: discovering the most significant changes in dataData visualization: using graphical methods to show patterns in data.CS 583 16Why is data mining important?Rapid computerization of businesses produce huge amount of dataHow to make best use of data?A growing realization: knowledge discovered from data can be used for competitive advantage.CS 583 17Why is data mining necessary?Make use of your data assetsThere is a big gap from stored data to knowledge; and the transition won’t occur automatically.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”CS 583 18Why data mining now?The data is abundant.The data is being warehoused.The computing power is affordable.The competitive pressure is strong.Data mining tools have become availableCS 583 19Related fieldsData mining is an emerging multi-disciplinary field:StatisticsMachine learningDatabasesInformation retrievalVisualizationetc.CS 583 20Data mining (KDD) processUnderstand the application domainIdentify data sources and select target dataPre-process: cleaning, attribute selectionData mining to extract patterns or modelsPost-process: identifying interesting or useful patternsIncorporate patterns in real world tasksCS 583 21Data mining applicationsMarketing,