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 


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  1. 1.
    Baker, L., McCallum, A.: Distributional clustering of words for text classification. In: Proc. of ACM SIGIR, pp. 96–103 (1998)Google Scholar
  2. 2.
    Bezdek, J.C.: Pattern recognition with fuzzy objective function algorithms. Plenum Press, New YorkGoogle Scholar
  3. 3.
    Bron, C., Kerbosch, J.: Finding all cliques of an undirected graph. Communications of the ACM 16, 575–577 (1971)CrossRefGoogle Scholar
  4. 4.
    Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to algorithms, 2nd edn. McGraw-Hill, New YorkGoogle Scholar
  5. 5.
    Cutting, D., Karger, D., Pedersen, J., Tukey, J.W.: Scatter/Gather: A cluster-based approach to browsing large document collections. In: Proc. of the 15th ACM SIGIR Conference, pp. 318–329 (1992)Google Scholar
  6. 6.
    Derenyi, I., Palla, G., Vicsek, T.: Clique percolation in random networks. Physics Review Letters 95, 160–202 (2005)Google Scholar
  7. 7.
    Dhillon, I.S.: Co-clustering documents and words using bipartite spectral graph partitioning. In: Proc. of the 7th ACM-KDD, pp. 269–274 (2001)Google Scholar
  8. 8.
    Ding, C.H.Q., He, X.F., Zha, H.Y., Gu, M., Simon, H.D.: A min-max cut algorithm for graph partitioning and data clustering. In: Proc. of IEEE ICDM, pp. 107–114 (2001)Google Scholar
  9. 9.
    Dorogovtsev, S.N., Mendes, J.F.F.: Evolution of networks. Oxford Press, New YorkGoogle Scholar
  10. 10.
    Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Computing Surveys 31, 264–323 (1999)CrossRefGoogle Scholar
  11. 11.
    King, B.: Step-wise clustering procedures. Journal of the American Statistical Association 69, 86–101 (1967)CrossRefGoogle Scholar
  12. 12.
    Krishnapuram, R., Joshi, A., Nasraoui, O., Yi, L.Y.: Low-complexity fuzzy relational clustering algorithms for web mining. IEEE Transactions on Fuzzy Systems 9, 595–607 (2001)CrossRefGoogle Scholar
  13. 13.
    Liu, X., Gong, Y.: Document clustering with clustering refinement and model selection capabilitities. In: Proc. of ACM SIGIR, pp. 191–198 (2002)Google Scholar
  14. 14.
    Palla, G., Derenyi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435, 814–818 (2005)CrossRefGoogle Scholar
  15. 15.
    Raghavan, V.V., Yu, C.T.: A comparison of the stability characteristics of some graph theoretic clustering methods. IEEE Transactions on Pattern Analysis and Machine Intelligence 3, 393–402 (1981)MATHCrossRefGoogle Scholar
  16. 16.
    Sneath, P.H.A., Sokal, R.R.: Numerical taxonomy: the principles and practice of numerical classification. Freeman, LondonGoogle Scholar
  17. 17.
    Steinbach, M., Karypis, G., Kumar, V.: A comparison of doucment clustering techniques. In: Proc. of KDD 2000 Workshop on Text Mining (2000)Google Scholar
  18. 18.
    Tsukiyama, S., Ide, M., Ariyoshi, H., Shirakawa, I.: A new algorithm for generating all the maximal independent sets. SIAM Journal on Computing 6, 505–517 (1977)MATHCrossRefMathSciNetGoogle Scholar
  19. 19.
    Zhao, Y., Karypis, G.: Criterion functions for document clustering. Technical Report #01-40, Department of Computer Science, University of MinnesotaGoogle Scholar

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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