Abstract

We present a non-hierarchal clustering algorithm that can determine the optimal number of clusters by using iterations of k-means and a stopping rule based on BIC. The procedure requires twice the computation of k-means. However, with no prior information about the number of clusters, our method is able to get the optimal clusters based on information theory instead of on a heuristic method.

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References

  1. 1.
    Huang, Zhexue: Extension to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values, Data Mining and Knowledge Discovery, 2[3] (1998) 283–304.Google Scholar
  2. 2.
    Hardy, Andre: On the Number of Clusters, Computational Statistics & Data Analysis, l[23] (1996) 83–96.CrossRefGoogle Scholar
  3. 3.
    Hartigan, J.A. and Wong, M.A.: A K-means clustering algorithm. Applied Statistics 28 (1979) 100–108.MATHCrossRefGoogle Scholar
  4. 4.
    MacQueen, J.B.: “Some methods for Classi_cation and Analysis of Multivariate Observations,” Proc. Symp. Math. Statist. and Probability, 5th Berkeley, 1 (1967) 281–297.Google Scholar
  5. 5.
    Pelleg, Dan and Andrew Moore: X-means: Extending K-means with Efficient Estimation of the Number of Clusters, ICML-2000 (2000).Google Scholar
  6. 6.
    Pelleg, Dan and Andrew Moore: Accelerating Exact k-means Algorithms with Geometric Reasoning, KDD-99 (1999).Google Scholar
  7. 7.
    Schwarz, G.: Estimating the dimension of a model, Ann. Statist., 6-2: (1978) 461–464.Google Scholar
  8. 8.
    Vesanto, Juha and Johan Himberg and Esa Alhoniemi and Juha Parhankangas: Self-Organizing Map in Matlab: the SOM Toolbox, Proceedings of the Matlab DSP Conference 1999, Espoo, Finland, November (1999) 35–40.Google Scholar
  9. 9.
    Yang, Ming-Hsuan and Narenda Ahuja: A Data Partition Method for Parallel Self-Organizing Map, Proceeding of the 1999 IEEE International Joint Conference on Neural Networks (IJCNN 99), Washington DC, July (1999).Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Tsunenori Ishioka
    • 1
  1. 1.National Center for University Entrance ExaminationsTokyoJapan

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