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Data Mining






Data Mining

Course Information

Instructors: Anand Rajaraman (anand @ kosmix dt com), Jeffrey D. Ullman (ullman @ gmail dt com).

Materials: There is no text. However, if you have the second edition of Database Systems: The Complete Book (Garcia-Molina, Ullman, Widom), you will find Section 20.2 and Chapters 22 and 23 relevant. Slides from the lectures will be made available in PPT and PDF formats.
Students will use the Gradiance automated homework system for which a fee will be charged. Note: if you already have Gradiance (GOAL) privileges from CS145 or CS245 within the past year, you should also have access to the CS345A homework without paying an additional fee. Notes and/or slides will be posted on-line.
You can see earlier versions of the notes and slides covering Data Mining. Not all these topics will be covered this year.

Click Here to Download :






DateTopicPowerPoint SlidesPDF Document
1/7Introductory Remarks (JDU)PPTPDF
1/7Introductory Remarks (AR)PPTPDF
1/12Map-ReducePPTPDF
1/14Frequent Itemsets 1PPTPDF
1/14-1/21Frequent Itemsets 2PPTPDF
1/16Peter Pawlowski's Talk on Aster DataPPTXPDF
1/16Nanda Kishore's Talk on ShareThisPPTPDF
1/26Recommendation SystemsPPTPDF
1/28Shingling, Minhashing, Locality-Sensitive HashingPPTPDF
2/2Applications and Variants of LSHPPTPDF
2/2-2/4Distance Measures, Generalizations of Minhashing and LSHPPTPDF
2/4High-Similarity AlgorithmsPPTPDF
2/9PageRankPPTPDF
2/11Link Spam, Hubs & AuthoritiesPPTPDF
2/18Generalization of Map-ReducePPTPDF
2/18-2/23ClusteringPPTPDF
2/23Streaming DataPPTPDF
2/25Relation ExtractionPPTPDF
3/2On-Line Algorithms, Advertising OptimizationPPTPDF
3/4Algorithms on StreamsPPTPDF


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