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dc.contributor.authorGürsoy, Attilaen_US
dc.contributor.authorCengiz, Ilkeren_US
dc.coverage.spatialManchester, UKen_US
dc.date.accessioned2016-02-08T11:57:58Z
dc.date.available2016-02-08T11:57:58Z
dc.date.issued2001en_US
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/11693/27617
dc.descriptionConference name: 7th International Euro-Par Conferenceen_US
dc.descriptionDate of Conference: August 28–31, 2001en_US
dc.description.abstractWe have developed and evaluated two parallelization schemes for a tree-based k-means clustering method on shared memory machines. One scheme is to partition the pattern space across processors. We have determined that spatial decomposition of patterns outperforms random decomposition even though random decomposition has almost no load imbalance problem. The other scheme is the parallel traverse of the search tree. This approach solves the load imbalance problem and performs slightly better than the spatial decomposition, but the efficiency is reduced due to thread synchronizations. In both cases, parallel treebased k-means clustering is significantly faster than the direct parallel k-means. © Springer-Verlag Berlin Heidelberg 2001.en_US
dc.language.isoEnglishen_US
dc.source.titleEuro-Par 2001 Parallel Processingen_US
dc.subjectClustering algorithmsen_US
dc.subjectDistributed computer systemsen_US
dc.subjectParallel architecturesen_US
dc.subjectK-means clusteringen_US
dc.subjectK-means clustering methoden_US
dc.subjectParallelizationsen_US
dc.subjectRandom decompositionen_US
dc.subjectShared memory architectureen_US
dc.subjectShared memory machinesen_US
dc.subjectSpatial decompositionsen_US
dc.subjectThread synchronizationen_US
dc.subjectMemory architectureen_US
dc.titleParallel pruning for k-means clustering on shared memory architecturesen_US
dc.typeConference Paperen_US
dc.departmentDepartment of Computer Engineering
dc.citation.spage321en_US
dc.citation.epage325en_US
dc.citation.volumeNumber2150en_US
dc.publisherSpringer Verlagen_US


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