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Article: Effect of data distribution in parallel mining of associations

TitleEffect of data distribution in parallel mining of associations
Authors
KeywordsAssociation rules
Data mining
Data skewness
Parallel computing
Parallel mining
Workload balance
Issue Date1999
PublisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=1384-5810
Citation
Data Mining And Knowledge Discovery, 1999, v. 3 n. 3, p. 291-314 How to Cite?
AbstractAssociation rule mining is an important new problem in data mining. It has crucial applications in decision support and marketing strategy. We proposed an efficient parallel algorithm for mining association rules on a distributed share-nothing parallel system. Its efficiency is attributed to the incorporation of two powerful candidate set pruning techniques. The two techniques, distributed and global prunings, are sensitive to two data distribution characteristics: data skewness and workload balance. The prunings are very effective when both the skewness and balance are high. We have implemented FPM on an IBM SP2 parallel system. The performance studies show that FPM outperforms CD consistently, which is a parallel version of the representative Apriori algorithm (Agrawal and Srikant, 1994). Also, the results have validated our observation on the effectiveness of the two pruning techniques with respect to the data distribution characteristics. Furthermore, it shows that FPM has nice scalability and parallelism, which can be tuned for different business applications. © 1999 Kluwer Academic Publishers.
Persistent Identifierhttp://hdl.handle.net/10722/118227
ISSN
2015 Impact Factor: 2.714
2015 SCImago Journal Rankings: 1.738
References

 

DC FieldValueLanguage
dc.contributor.authorCheung, DWen_HK
dc.contributor.authorXiao, Yen_HK
dc.date.accessioned2010-09-26T07:55:11Z-
dc.date.available2010-09-26T07:55:11Z-
dc.date.issued1999en_HK
dc.identifier.citationData Mining And Knowledge Discovery, 1999, v. 3 n. 3, p. 291-314en_HK
dc.identifier.issn1384-5810en_HK
dc.identifier.urihttp://hdl.handle.net/10722/118227-
dc.description.abstractAssociation rule mining is an important new problem in data mining. It has crucial applications in decision support and marketing strategy. We proposed an efficient parallel algorithm for mining association rules on a distributed share-nothing parallel system. Its efficiency is attributed to the incorporation of two powerful candidate set pruning techniques. The two techniques, distributed and global prunings, are sensitive to two data distribution characteristics: data skewness and workload balance. The prunings are very effective when both the skewness and balance are high. We have implemented FPM on an IBM SP2 parallel system. The performance studies show that FPM outperforms CD consistently, which is a parallel version of the representative Apriori algorithm (Agrawal and Srikant, 1994). Also, the results have validated our observation on the effectiveness of the two pruning techniques with respect to the data distribution characteristics. Furthermore, it shows that FPM has nice scalability and parallelism, which can be tuned for different business applications. © 1999 Kluwer Academic Publishers.en_HK
dc.languageengen_HK
dc.publisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=1384-5810en_HK
dc.relation.ispartofData Mining and Knowledge Discoveryen_HK
dc.subjectAssociation rulesen_HK
dc.subjectData miningen_HK
dc.subjectData skewnessen_HK
dc.subjectParallel computingen_HK
dc.subjectParallel miningen_HK
dc.subjectWorkload balanceen_HK
dc.titleEffect of data distribution in parallel mining of associationsen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1384-5810&volume=&spage=&epage=&date=1999&atitle=Effect+of+Data+Distribution+in+Parallel+Mining+of+Associationsen_HK
dc.identifier.emailCheung, DW:dcheung@cs.hku.hken_HK
dc.identifier.authorityCheung, DW=rp00101en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.scopuseid_2-s2.0-27144466086en_HK
dc.identifier.hkuros47948en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-27144466086&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume3en_HK
dc.identifier.issue3en_HK
dc.identifier.spage291en_HK
dc.identifier.epage314en_HK
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridCheung, DW=34567902600en_HK
dc.identifier.scopusauthoridXiao, Y=22735880100en_HK

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