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Parallel k/h-Means Clustering for Large Data Sets

  • Kilian Stoffel
  • Abdelkader Belkoniene
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1685)

Abstract

This paper describes the realization of a parallel version of the k/h-means clustering algorithm. This is one of the basic algorithms used in a wide range of data mining tasks. We show how a database can be distributed and how the algorithm can be applied to this distributed database. The tests conducted on a network of 32 PCs showed for large data sets a nearly ideal speedup.

Keywords

Execution Time Parallel Version Data Mining Task Distribute Computing Environment Machine Learn Database 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

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Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Kilian Stoffel
    • 1
  • Abdelkader Belkoniene
    • 1
  1. 1.Groupe InformatiqueUniversité de NeuchâtelNeuchâtelSwitzerland

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