Improving the performance of k-means clustering through computation skipping and data locality optimizations

Orhan Kislal, Piotr Berman, Mahmut Kandemir

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

We present three different optimization techniques for k-means clustering algorithm to improve the running time without decreasing the accuracy of the cluster centers significantly. Our first optimization restructures loops to improve cache behavior when executing on multicore architectures. The remaining two optimizations skip select points to reduce execution latency. Our sensitivity analysis suggests that the performance can be enhanced through a good understanding of the data and careful configuration of the parameters.

Original languageEnglish (US)
Title of host publicationCF '12 - Proceedings of the ACM Computing Frontiers Conference
Pages273-275
Number of pages3
DOIs
StatePublished - Jun 28 2012
EventACM Computing Frontiers Conference, CF '12 - Cagliari, Italy
Duration: May 15 2012May 17 2012

Publication series

NameCF '12 - Proceedings of the ACM Computing Frontiers Conference

Other

OtherACM Computing Frontiers Conference, CF '12
Country/TerritoryItaly
CityCagliari
Period5/15/125/17/12

All Science Journal Classification (ASJC) codes

  • Software

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