15-frequentpattern

15-frequentpattern - Frequent Pattern CS273 Data and...

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Frequent Pattern CS273 - Data and Knowledge Bases Xifeng Yan Computer Science niversity of California at Santa Barbara University of California at Santa Barbara
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Department of Computer Science Homework 3 will be posted on Nov 18, Due on Dec 1. Readings: J. Han, J. Pei, and Y. Yin, '' Mining Frequent Patterns without Candidate Generation, Proc. 2000 ACM-SIGMOD Int. Conf. on anagement of Data 2000 Management of Data, 2000 Data and Knowledge Bases | University of California at Santa Barbara 2
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Department of Computer Science What Is Frequent Pattern? Frequent pattern: a pattern (a set of items, subsequences, substructures, etc.) that occurs frequently in a data set First proposed by Agrawal, Imielinski, and Swami in the context of frequent itemsets and association rule mining oti ation Finding inherent reg larities in data Motivation: Finding inherent regularities in data What products were often purchased together?— Beer and diapers?! hat are the subsequent purchases after buying a PC? What are the subsequent purchases after buying a PC? What are the common API call sets? Applications Basket data analysis, cross-marketing, catalog design, sale campaign analysis, Web log (click stream) analysis, and DNA sequence analysis . Data and Knowledge Bases | University of California at Santa Barbara 3 slides by courtesy of J. Han with modifications
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Department of Computer Science Google Acquire -> ? Data and Knowledge Bases | University of California at Santa Barbara 4
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Department of Computer Science Why Is Freq. Pattern Mining Important? Discloses an intrinsic and basic property of data sets Forms the foundation for many essential data mining tasks Association, correlation, and causality analysis Sequential, structural (e.g., sub-graph) patterns Pattern analysis in spatiotemporal, multimedia, time-series, and stream data Classification: associative classification, pattern-based classification luster analysis: frequent pattern- ased clustering Cluster analysis: frequent pattern based clustering Data warehousing: iceberg cube and cube-gradient Semantic data compression: fascicles Broad applications Data and Knowledge Bases | University of California at Santa Barbara 5
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Department of Computer Science Frequent Patterns and Association Rules Itemset X = {x 1 , …, x k } Find all the rules X Y with minimum Transaction-id Items bought 10 A, B, D support and confidence Support , s , probability that a transaction contains X Y 20 A, C, D 30 A, D, E 40 B, E, F Confidence , c, conditional probability that a transaction having X also contains Y Customer Customer b t h 50 B, C, D, E, F Let sup min = 50%, conf = 50% Freq. Pat.: {A:3, B:3, D:4, E:3, AD:3} buys diaper buys both Association rules: A D (60%, 100%) (60%, 75%) Customer uys beer Data and Knowledge Bases | University of California at Santa Barbara 6 buys beer
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Department of Computer Science Scalable Methods for Mining Frequent Patterns The downward closure property of frequent patterns ny subset of a frequent itemset must be frequent
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This note was uploaded on 01/09/2012 for the course CS CS273 taught by Professor Xifengyan during the Spring '11 term at UCSB.

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15-frequentpattern - Frequent Pattern CS273 Data and...

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