CS583 – Data Mining and Text MiningGeneral InformationCourse structureGradingPrerequisitesTeaching materialsTopicsFeedback and suggestionsRules and PoliciesIntroduction to the courseWhat is data mining?Classic data mining tasksClassic data mining tasks (contd)Why is data mining important?Why is data mining necessary?Why data mining?Related fieldsData mining (KDD) processData mining applicationsText miningResourcesProject assignmentsCS583 – Data Mining and Text MiningCourse Web Pagehttp://www.cs.uic.edu/~liub/teach/cs583-fall-11/cs583.htmlCS583, Bing Liu, UIC2General InformationInstructor: Bing Liu Email: [email protected] Tel: (312) 355 1318 Office: SEO 931 Course Call Number: 30286 Lecture times: 3:30pm-4:45pm, Tuesday and Thursday Room: A3 LCOffice hours: 2:00pm-3:30pm, Tuesday & Thursday (or by appointment)CS583, Bing Liu, UIC3Course structureThe course has two parts: Lectures - Introduction to the main topicsTwo projects (done in groups)1 programming project.1 research project.Lecture slides are available on the course web page.CS583, Bing Liu, UIC4GradingFinal Exam: 40% Midterm: 20% 1 midtermProjects: 40% 1 programming (15%).1 research assignment (25%)CS583, Bing Liu, UIC5Prerequisites Knowledge of basic probability theory algorithmsCS583, Bing Liu, UIC6Teaching materials Required Text Web Data Mining: Exploring Hyperlinks, Contents and Usage data. By Bing Liu, Second Edition, Springer, ISBN 978-3-642-19459-7. References: Data mining: Concepts and Techniques, by Jiawei Han and Micheline Kamber, Morgan Kaufmann, ISBN 1-55860-489-8. Introduction to Data Mining, by Pang-Ning Tan, Michael Steinbach, and Vipin Kumar, Pearson/Addison Wesley, ISBN 0-321-32136-7. Principles of Data Mining, by David Hand, Heikki Mannila, Padhraic Smyth, The MIT Press, ISBN 0-262-08290-X. Machine Learning, by Tom M. Mitchell, McGraw-Hill, ISBN 0-07-042807-7CS583, Bing Liu, UIC7TopicsIntroductionData pre-processingAssociation rules and sequential patterns Classification (supervised learning) Clustering (unsupervised learning) Partially (semi-) supervised learningInformation retrieval and Web search Social network analysisOpinion mining and sentiment analysis Recommender systems and collaborative filtering Web data extractionCS583, Bing Liu, UIC8Feedback and suggestionsYour feedback and suggestions are most welcome!I need it to adapt the course to your needs.Let me know if you find any errors in the textbook.Share your questions and concerns with the class – very likely others may have the same.No pain no gainThe more you put in, the more you getYour grades are proportional to your efforts.CS583, Bing Liu, UIC9Rules 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.Introduction to the courseCS583, Bing Liu, UIC11What is data mining?Data mining is also called knowledge discovery and data mining (KDD)Data mining isextraction of useful patterns from data sources, e.g., databases, texts, web, images, etc. Patterns must be:valid, novel, potentially useful, understandableCS583, Bing Liu, UIC12Classic data mining tasksClassification:mining patterns that can classify future (new) data into known classes. Association rule miningmining any rule of the form X Y, where X and Y are sets of data items. E.g., Cheese, Milk Bread [sup =5%, confid=80%]Clusteringidentifying a set of similarity groups in the dataCS583, Bing Liu, UIC13Classic data mining tasks (contd)Sequential pattern mining:A sequential rule: A B, says that event A will be immediately followed by event B with a certain confidenceDeviation detection: discovering the most significant changes in dataData visualization: using graphical methods to show patterns in data.CS583, Bing Liu, UIC14Why is data mining important?Computerization of businesses produce huge amount of dataHow to make best use of data?Knowledge discovered from data can be used for competitive advantage.Online e-businesses are generate even larger data setsOnline retailers (e.g., amazon.com) are largely driving by data mining.Web search engines are information retrieval (text mining) and data mining companiesCS583, Bing Liu, UIC15Why is data mining necessary?Make use of your data assetsThere is a big gap from stored data to knowledge; and the transition won’t occur automatically.Many interesting things that one wants to find cannot be found using database queries“find people likely to buy my products”“Who are likely to respond to my promotion”“Which movies should be recommended to each customer?”CS583, Bing Liu, UIC16Why data mining?The data is abundant.The computing power is not an issue.Data mining tools are availableThe competitive pressure is very strong.Almost every company is doing (or has to do) itCS583, Bing Liu, UIC17Related fieldsData mining is an multi-disciplinary field:Machine learningStatisticsDatabasesInformation retrievalVisualizationNatural language processingetc.CS583, Bing Liu, UIC18Data mining (KDD) processUnderstand the application domainIdentify data sources and select target dataPre-processing: cleaning, attribute selection, etcData mining to extract patterns or modelsPost-processing: identifying interesting or useful patterns/knowledgeIncorporate patterns/knowledge in real world tasksCS583, Bing Liu, UIC19Data mining applicationsMarketing, customer profiling and retention, identifying potential customers, market segmentation. Engineering: identify causes of problems in
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