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Article: K-means-type algorithms on distributed memory computer

TitleK-means-type algorithms on distributed memory computer
Authors
KeywordsClustering
Data mining
K-means-type algorithm
Parallel algorithms
Issue Date2000
Citation
International Journal of High Speed Computing, 2000, v. 11, n. 2, p. 75-91 How to Cite?
AbstractPartitioning a set of objects into homogeneous clusters is a fundamental operation in data mining. The k-means-type algorithm is best suited for implementing this operation because of its efficiency in clustering large numerical and categorical data sets. An efficient parallel k-means-type algorithm for clustering data sets on a distributed share-nothing parallel system is considered. It has a simple communication scheme which performs only one round of information exchange in every iteration. We show that the speedup of our algorithm is asymptotically linear when the number of objects is sufficiently large. We implement the parallel k-means-type algorithm on an IBM SP2 parallel machine. The performance studies show that the algorithm has nice parallelism in experiments.
Persistent Identifierhttp://hdl.handle.net/10722/276541
ISSN
2001 Impact Factor: 0.037
2007 SCImago Journal Rankings: 0.196

 

DC FieldValueLanguage
dc.contributor.authorNg, M. K.-
dc.date.accessioned2019-09-18T08:33:55Z-
dc.date.available2019-09-18T08:33:55Z-
dc.date.issued2000-
dc.identifier.citationInternational Journal of High Speed Computing, 2000, v. 11, n. 2, p. 75-91-
dc.identifier.issn0129-0533-
dc.identifier.urihttp://hdl.handle.net/10722/276541-
dc.description.abstractPartitioning a set of objects into homogeneous clusters is a fundamental operation in data mining. The k-means-type algorithm is best suited for implementing this operation because of its efficiency in clustering large numerical and categorical data sets. An efficient parallel k-means-type algorithm for clustering data sets on a distributed share-nothing parallel system is considered. It has a simple communication scheme which performs only one round of information exchange in every iteration. We show that the speedup of our algorithm is asymptotically linear when the number of objects is sufficiently large. We implement the parallel k-means-type algorithm on an IBM SP2 parallel machine. The performance studies show that the algorithm has nice parallelism in experiments.-
dc.languageeng-
dc.relation.ispartofInternational Journal of High Speed Computing-
dc.subjectClustering-
dc.subjectData mining-
dc.subjectK-means-type algorithm-
dc.subjectParallel algorithms-
dc.titleK-means-type algorithms on distributed memory computer-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1142/S0129053300000096-
dc.identifier.scopuseid_2-s2.0-0034196966-
dc.identifier.volume11-
dc.identifier.issue2-
dc.identifier.spage75-
dc.identifier.epage91-

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