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Article: An adaptive algorithm for mining association rules on shared-memory parallel machines

TitleAn adaptive algorithm for mining association rules on shared-memory parallel machines
Authors
KeywordsAssociation Rules
Data Mining
Parallel Computing
Parallel Databases
Parallel Mining
Sharedmemory Multi-Processors
Issue Date2001
PublisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0926-8782
Citation
Distributed And Parallel Databases, 2001, v. 9 n. 2, p. 99-132 How to Cite?
AbstractMining association rules from large databases is very costly. We propose to develop parallel algorithms for this task on shared-memory multiprocessor (SMP). All proposed parallel algorithms for other paradigms follow the conventional level-wise approach: they need as many iterations as the length of the maximum large itemset. To make matter worse, they impose a synchronization in every iteration which would cause serious I/O contention on shared-memory parallel system. An adaptive asynchronous parallel mining algorithm APM has been proposed for SMP. All processors generate candidates dynamically and count itemset supports independently without synchronization. Two optimization techniques have been proposed for the reduction of database scanning and the number of candidates. The algorithm APM has been implemented on a Sun Enterprise 4000 shared-memory multiprocessor with 12 nodes. The experiments show that the optimizations have very good effects and APM has a substantial lead in performance over other proposed level-wise algorithms.
Persistent Identifierhttp://hdl.handle.net/10722/152281
ISSN
2015 Impact Factor: 0.8
2015 SCImago Journal Rankings: 0.593
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorCheung, DWen_US
dc.contributor.authorHu, Ken_US
dc.contributor.authorXia, Sen_US
dc.date.accessioned2012-06-26T06:36:55Z-
dc.date.available2012-06-26T06:36:55Z-
dc.date.issued2001en_US
dc.identifier.citationDistributed And Parallel Databases, 2001, v. 9 n. 2, p. 99-132en_US
dc.identifier.issn0926-8782en_US
dc.identifier.urihttp://hdl.handle.net/10722/152281-
dc.description.abstractMining association rules from large databases is very costly. We propose to develop parallel algorithms for this task on shared-memory multiprocessor (SMP). All proposed parallel algorithms for other paradigms follow the conventional level-wise approach: they need as many iterations as the length of the maximum large itemset. To make matter worse, they impose a synchronization in every iteration which would cause serious I/O contention on shared-memory parallel system. An adaptive asynchronous parallel mining algorithm APM has been proposed for SMP. All processors generate candidates dynamically and count itemset supports independently without synchronization. Two optimization techniques have been proposed for the reduction of database scanning and the number of candidates. The algorithm APM has been implemented on a Sun Enterprise 4000 shared-memory multiprocessor with 12 nodes. The experiments show that the optimizations have very good effects and APM has a substantial lead in performance over other proposed level-wise algorithms.en_US
dc.languageengen_US
dc.publisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0926-8782en_US
dc.relation.ispartofDistributed and Parallel Databasesen_US
dc.subjectAssociation Rulesen_US
dc.subjectData Miningen_US
dc.subjectParallel Computingen_US
dc.subjectParallel Databasesen_US
dc.subjectParallel Miningen_US
dc.subjectSharedmemory Multi-Processorsen_US
dc.titleAn adaptive algorithm for mining association rules on shared-memory parallel machinesen_US
dc.typeArticleen_US
dc.identifier.emailCheung, DW:dcheung@cs.hku.hken_US
dc.identifier.authorityCheung, DW=rp00101en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1023/A:1018951022124en_US
dc.identifier.scopuseid_2-s2.0-0035276901en_US
dc.identifier.hkuros58048-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0035276901&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume9en_US
dc.identifier.issue2en_US
dc.identifier.spage99en_US
dc.identifier.epage132en_US
dc.identifier.isiWOS:000168429600001-
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridCheung, DW=34567902600en_US
dc.identifier.scopusauthoridHu, K=7203085144en_US
dc.identifier.scopusauthoridXia, S=7202893313en_US

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