Parallel Algorithm for Extended Star Clustering

  • Reynaldo Gil-García
  • José M. Badía-Contelles
  • Aurora Pons-Porrata
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3287)


In this paper we present a new parallel clustering algorithm based on the extended star clustering method. This algorithm can be used for example to cluster massive data sets of documents on distributed memory multiprocessors. The algorithm exploits the inherent data-parallelism in the extended star clustering algorithm. We implemented our algorithm on a cluster of personal computers connected through a Myrinet network. The code is portable to different architectures and it uses the MPI message-passing library. The experimental results show that the parallel algorithm clearly improves its sequential version with large data sets. We show that the speedup of our algorithm approaches the optimal as the number of objects increases.


Cluster Algorithm Parallel Algorithm Star Cluster Subspace Cluster Minimum Vertex Cover 
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 2004

Authors and Affiliations

  • Reynaldo Gil-García
    • 1
  • José M. Badía-Contelles
    • 2
  • Aurora Pons-Porrata
    • 1
  1. 1.Universidad de OrienteSantiago de CubaCuba
  2. 2.Universitat Jaume ICastellónSpain

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