Clique Percolation Method for Finding Naturally Cohesive and Overlapping Document Clusters

  • Wei Gao
  • Kam-Fai Wong
  • Yunqing Xia
  • Ruifeng Xu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4285)


Techniques for find document clusters mostly depend on models that impose strong explicit and/or implicit priori assumptions. As a consequence, the clustering effects tend to be unnatural and stray away from the intrinsic grouping natures of a document collection. We apply a novel graph-theoretic technique called Clique Percolation Method (CPM) for document clustering. In this method, a process of enumerating highly cohesive maximal document cliques is performed in a random graph, where those strongly adjacent cliques are mingled to form naturally overlapping clusters. Our clustering results can unveil the inherent structural connections of the underlying data. Experiments show that CPM can outperform some typical algorithms on benchmark data sets, and shed light on its advantages on natural document clustering.


Random Graph Degree Distribution Original Graph Hierarchical Agglomerative Cluster Document Cluster 
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

  • Wei Gao
    • 1
  • Kam-Fai Wong
    • 1
  • Yunqing Xia
    • 1
  • Ruifeng Xu
    • 1
  1. 1.Department of Systems Engineering and Engineering ManagementThe Chinese University of Hong KongHong KongChina

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