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An Adaptive Parallel Hierarchical Clustering Algorithms without Memory Conflicts

论文摘要

Clustering of data has numerous applications and has been studied extensively. It is very important in Bio-informatics and data mining. Though many parallel algorithms have been designed, most of algorithms use the CRCW-PRAM or CREW-PRAM models of computing. This paper proposes a parallel EREW deterministic algorithm for hierarchical clustering. Based on algorithms of complete graph and Euclidean minimum spanning tree, the proposed algorithms can cluster n objects with O（p） processors in O（n2/p） time where 1≤p≤n/logn. Performance comparisons show that our algorithm is the first algorithm that is both without memory conflicts and adaptive.

论文目录

  • Abstract
  • Chapter 1 Introduction
  • 1.1 Introduction
  • 1.2 What is Parallel Computing
  • 1.3 What’s Parallel Computer
  • 1.4 What is Parallel Programming
  • 1.5 What is the Clustering
  • Chapter 2 Preliminaries
  • 2.1 Models Of Computing
  • 2.2 Hierarchical Clustering
  • 2.3 Distributed Computing and Super Computers
  • 2.3.1 Distributed Computing
  • 2.3.2 Super Computers
  • Chapter 3 The Proposed Algorithms
  • 3.1 Introduction
  • 3.2 Construct a Complete Graph in Parallel
  • 3.3 Generatic Of MST
  • 3.4 Pruning edges and finding connected compenents
  • 3.5 The proposed parallel hierarchical clustering algorithm
  • Chapter 4 Comparison And Conclusion
  • 4.1 Performance comparisons
  • 4.2 Conclusions
  • 4.3 Future Plan
  • Acknowledgements
  • References

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