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.

Date	Topic	PowerPoint Slides	PDF Document
1/7	Introductory Remarks (JDU)	PPT	PDF
1/7	Introductory Remarks (AR)	PPT	PDF
1/12	Map-Reduce	PPT	PDF
1/14	Frequent Itemsets 1	PPT	PDF
1/14-1/21	Frequent Itemsets 2	PPT	PDF
1/16	Peter Pawlowski's Talk on Aster Data	PPTX	PDF
1/16	Nanda Kishore's Talk on ShareThis	PPT	PDF
1/26	Recommendation Systems	PPT	PDF
1/28	Shingling, Minhashing, Locality-Sensitive Hashing	PPT	PDF
2/2	Applications and Variants of LSH	PPT	PDF
2/2-2/4	Distance Measures, Generalizations of Minhashing and LSH	PPT	PDF
2/4	High-Similarity Algorithms	PPT	PDF
2/9	PageRank	PPT	PDF
2/11	Link Spam, Hubs & Authorities	PPT	PDF
2/18	Generalization of Map-Reduce	PPT	PDF
2/18-2/23	Clustering	PPT	PDF
2/23	Streaming Data	PPT	PDF
2/25	Relation Extraction	PPT	PDF
3/2	On-Line Algorithms, Advertising Optimization	PPT	PDF
3/4	Algorithms on Streams	PPT	PDF