CS583 – Data Mining and Text MiningGeneral InformationCourse structurePaper presentationProgramming 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 applicationsCS 594 1CS583 – Data Mining and Text MiningCourse Web Pagehttp://www.cs.uic.edu/~liub/teach/cs583-spring-05/cs583.htmlCS 594 2General InformationInstructor: Bing Liu Email: [email protected] Tel: (312) 355 1318 Office: SEO 931 Course Call Number: 19696 Lecture times: 3:30pm – 4:45pm, Tuesday and Thursday Room: 208 GH Office hours: 3:30pm - 5:00pm Monday (or by appointment)CS 594 3Course structureThe course has three parts: Lectures - Introduction to the main topicsResearch Paper Presentation Students read papers, and present in classProgramming projects2 programming assignments.To be demonstrated to meLecture slides and other relevant information will be made available at the course web siteCS 594 4Paper presentation2 people in a group. Each group reads one paper and gives a in-class presentation of the paper. Every member should actively participate in the presentation. Marks will be given individually. Presentation duration to be determined.CS 594 5Programming 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 594 6GradingFinal Exam: 40% Midterm: 30% 1 midtermProgramming projects: 20% 2 programming assignments.Research paper presentation: 10%CS 594 7Prerequisites Knowledge of probability and algorithmsCS 594 8Teaching materials Main Text Data mining: Concepts and Techniques, by Jiawei Han and Micheline Kamber, Morgan Kaufmann Publishers, ISBN 1-55860-489-8. References: 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 Other reading materials (the list will be given to you later) Data mining resource site: KDnuggets DirectoryCS 594 9TopicsData pre-processingAssociation rule mining Classification (supervised learning) Clustering (unsupervised learning) Introduction to some other data mining tasks Post-processing of data mining resultsText mining Partial/Semi-supervised learningIntroduction to Web miningCS 594 10Any 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 for data mining.The more you put in, the more you getYour grades are proportional to your efforts.CS 594 11Rules 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 (this includes, exams and program) 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. MOSS: Sharing code with your classmates is not acceptable!!! All programs will be screened using the Moss (Measure of Software Similarity.) system. 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 594 12Introduction to Data MiningCS 594 13What 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 594 14Example 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 594 15Main 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 594 16Main 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 594 17Why 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 594 18Why 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 594 19Why 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 594 20Related fieldsData mining is an emerging multi-disciplinary field:StatisticsMachine learningDatabasesInformation retrievalVisualizationetc.CS 594 21Data 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: