DOC PREVIEW
UIC CS 583 - LECTURE NOTES

This preview shows page 1-2-21-22 out of 22 pages.

Save
View full document
View full document
Premium Document
Do you want full access? Go Premium and unlock all 22 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 22 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 22 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 22 pages.
Access to all documents
Download any document
Ad free experience
Premium Document
Do you want full access? Go Premium and unlock all 22 pages.
Access to all documents
Download any document
Ad free experience

Unformatted text preview:

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 InformationInstructor: 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 LCOffice hours: 2:00pm-3:30pm, Tuesday & Thursday (or by appointment)CS583, Bing Liu, UIC3Course structureThe course has two parts: Lectures - Introduction to the main topicsTwo projects (done in groups)1 programming project.1 research project.Lecture slides are available on the course web page.CS583, Bing Liu, UIC4GradingFinal Exam: 40% Midterm: 20% 1 midtermProjects: 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, UIC7TopicsIntroductionData pre-processingAssociation rules and sequential patterns Classification (supervised learning) Clustering (unsupervised learning) Partially (semi-) supervised learningInformation retrieval and Web search Social network analysisOpinion mining and sentiment analysis Recommender systems and collaborative filtering Web data extractionCS583, Bing Liu, UIC8Feedback and suggestionsYour 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 gainThe more you put in, the more you getYour 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 isextraction 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 tasksClassification: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 confidenceDeviation detection: discovering the most significant changes in dataData visualization: using graphical methods to show patterns in data.CS583, Bing Liu, UIC14Why is data mining important?Computerization of businesses produce huge amount of dataHow to make best use of data?Knowledge discovered from data can be used for competitive advantage.Online e-businesses are generate even larger data setsOnline 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 assetsThere 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 availableThe competitive pressure is very strong.Almost every company is doing (or has to do) itCS583, Bing Liu, UIC17Related fieldsData mining is an multi-disciplinary field:Machine learningStatisticsDatabasesInformation retrievalVisualizationNatural language processingetc.CS583, Bing Liu, UIC18Data mining (KDD) processUnderstand the application domainIdentify data sources and select target dataPre-processing: cleaning, attribute selection, etcData mining to extract patterns or modelsPost-processing: identifying interesting or useful patterns/knowledgeIncorporate patterns/knowledge in real world tasksCS583, Bing Liu, UIC19Data mining applicationsMarketing, customer profiling and retention, identifying potential customers, market segmentation. Engineering: identify causes of problems in


View Full Document

UIC CS 583 - LECTURE NOTES

Download LECTURE NOTES
Our administrator received your request to download this document. We will send you the file to your email shortly.
Loading Unlocking...
Login

Join to view LECTURE NOTES and access 3M+ class-specific study document.

or
We will never post anything without your permission.
Don't have an account?
Sign Up

Join to view LECTURE NOTES 2 2 and access 3M+ class-specific study document.

or

By creating an account you agree to our Privacy Policy and Terms Of Use

Already a member?