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Joint Cluster Based Co-clustering for Clustering Ensembles

  • Tianming Hu
  • Liping Liu
  • Chao Qu
  • Sam Yuan Sung
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4093)

Abstract

This paper introduces a new method for solving clustering ensembles, that is, combining multiple clusterings over a common dataset into a final better one. The ensemble is reduced to a graph that simultaneously models as vertices the original clusters in the ensemble and the joint clusters derived from them. Only edges linking vertices from different types are considered. The resulting graph can be partitioned efficiently to produce the final clustering. Finally, the proposed method is evaluated against two graph formulations commonly used.

Keywords

Consensus Function Normalize Mutual Information Graph Partitioning True Cluster Cluster Ensemble 
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 2006

Authors and Affiliations

  • Tianming Hu
    • 1
  • Liping Liu
    • 1
  • Chao Qu
    • 1
  • Sam Yuan Sung
    • 2
  1. 1.Department of Computer ScienceDongGuan University of TechnologyDongGuanChina
  2. 2.Department of Computer ScienceSouth Texas UniversityMcAllenUSA

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