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A Data-Clustering Algorithm on Distributed Memory Multiprocessors

  • Inderjit S. Dhillon
  • Dharmendra S. Modha
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1759)

Abstract

To cluster increasingly massive data sets that are common today in data and text mining, we propose a parallel implementation of the k-means clustering algorithm based on the message passing model. The proposed algorithm exploits the inherent data-parallelism in the k-means algorithm. We analytically show that the speedup and the scaleup of our algorithm approach the optimal as the number of data points increases. We implemented our algorithm on an IBM POWERparallel SP2 with a maximum of 16 nodes. On typical test data sets, we observe nearly linear relative speedups, for example, 15.62 on 16 nodes, and essentially linear scaleup in the size of the data set and in the number of clusters desired. For a 2 gigabyte test data set, our implementation drives the 16 node SP2 at more than 1.8 gigaflops.

Keywords

Execution Time Association Rule Parallelization Strategy Relative Speedup Proposed Parallel Algorithm 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Inderjit S. Dhillon
    • 1
  • Dharmendra S. Modha
    • 2
  1. 1.Department of Computer ScienceUniversity of TexasAustinUSA
  2. 2.IBM Almaden Research CenterSan JoseUSA

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