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      • Bilkent Theses
      • Theses - Department of Computer Engineering
      • Dept. of Computer Engineering - Master's degree
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      Data decomposition techniques for parallel tree-based k-means clustering

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      Author
      Şen, Cenk
      Advisor
      Gürsoy, Atilla
      Date
      2002
      Publisher
      Bilkent University
      Language
      English
      Type
      Thesis
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      Abstract
      The main computation in the k-means clustering is distance calculations between cluster centroids and patterns. As the number of the patterns and the number of centroids increases, time needed to complete computations increased. This computational load requires high performance computers and/or algorithmic improvements. The parallel tree-based k-means algorithm on distributed memory machines combines the algorithmic improvements and high computation capacity of the parallel computers to deal with huge datasets. Its performance is affected by the data decomposition technique used. In this thesis, we presented novel data decomposition technique to improve the performance of the parallel tree-based k-means algorithm on distributed memory machines. Proposed tree-based decomposition techniques try to decrease the total number of the distance calculations by assigning processors compact subspaces. The compact subspace improves the performance of the pruning function of the tree-based k-means algorithm. We have implemented the algorithm and have conducted experiments on a PC cluster. Our experimental results demonstrated that the tree-based decomposition technique outperforms the random decomposition and stripwise decomposition techniques.
      Keywords
      Clustering
      Parallel algorithm
      Load balancing
      Data decomposition
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      http://hdl.handle.net/11693/15853
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