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introduction - Web Mining Seminar CSE 450 Spring 2008 MWF...

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Web Mining Seminar Spring 2008 Spring 2008 MWF 11:10-12:00pm Maginnes 113 MWF 11:10-12:00pm Maginnes 113 Instructor:  Instructor:  Dr. Brian D. Davison Dr. Brian D. Davison Dept. of Computer Science & Engineering Dept. of Computer Science & Engineering Lehigh University Lehigh University [email protected] [email protected] http://www.cse.lehigh.edu/~brian/course/webmining/ http://www.cse.lehigh.edu/~brian/course/webmining/ CSE 450
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Spring 2008 Web Mining Seminar 2 Course Objectives To gain a background in web mining techniques To become proficient at reading technical papers To gain knowledge of important current web mining research To gain experience presenting technical material To learn to write critical reviews of research papers To explore a research project in some depth and write and present a technical paper summarizing that work
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Spring 2008 Web Mining Seminar 3 Teaching materials Required Text: Web Data Mining : Exploring Hyperlinks, Contents and Usage data. By Bing Liu, Springer, ISBN 3-450-37881-2. Optional Text: Data Mining : Practical Machine Learning Tools and Techniques, 2 nd Ed. By Witten and Frank, Morgan Kaufmann Papers: Most (perhaps all) available online Author's homepages Citeseer/ResearchIndex Google Scholar ACM Digital Library IEEExplore
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Spring 2008 Web Mining Seminar 4 Seminars are less formal We have a small class Introduce yourselves!
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Spring 2008 Web Mining Seminar 5 Introduction to Web Mining
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Spring 2008 Web Mining Seminar 6 What 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, understandable
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Spring 2008 Web Mining Seminar 7 Classic data mining tasks Classification: mining patterns that can classify future (new) data into known classes. Association rule mining mining 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%] Clustering identifying a set of similarity groups in the data Sequential pattern mining: A sequential rule: A B , says that event A will be immediately followed by event B with a certain confidence
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