A Latent Variable Pairwise Classification Model of a Clustering Ensemble

  • Vladimir Berikov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6713)


This paper addresses some theoretical properties of clustering ensembles. We consider the problem of cluster analysis from pattern recognition point of view. A latent variable pairwise classification model is proposed for studying the efficiency (in terms of ”error probability”) of the ensemble. The notions of stability, homogeneity and correlation between ensemble elements are introduced. An upper bound for misclassification probability is obtained. Numerical experiment confirms potential usefulness of the suggested ensemble characteristics.


clustering ensemble latent variable model misclassification probability error bound ensemble’s homogeneity and correlation 


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  1. 1.
    Jain, A.K.: Data Clustering: 50 Years Beyond K-Means. Pattern Recognition Letters 31(8), 651–666 (2010)CrossRefGoogle Scholar
  2. 2.
    Strehl, A., Ghosh, J.: Clustering ensembles - a knowledge reuse framework for combining multiple partitions. The Journal of Machine Learning Research 3, 583–617 (2002)zbMATHGoogle Scholar
  3. 3.
    Kuncheva, L.I., Rodriguez, J.J., Plumpton, C.O., Linden, D.E.J., Johnston, S.J.: Random Subspace Ensembles for fMRI Classification. IEEE Transactions on Medical Imaging 29(2), 531–542 (2010)CrossRefGoogle Scholar
  4. 4.
    Pestunov, I.A., Berikov, V.B., Kulikova, E.A.: Grid-based ensemble clustering algorithm using sequence of fixed grids. In: Proc. of the 3rd IASTED Intern. Conf. on Automation, Control, and Information Technology, pp. 103–110. ACTA Press, Calgary (2010)Google Scholar
  5. 5.
    Iam-on, N., Boongoen, T., Garrett, S.: LCE: a link-based cluster ensemble method for improved gene expression data analysis. Bioinformatics 26(12), 1513–1519 (2010)CrossRefGoogle Scholar
  6. 6.
    Hong, Y., Kwong, S.: To combine steady-state genetic algorithm and ensemble learning for data clustering. Pattern Recognition Letters 29(9), 1416–1423 (2008)CrossRefGoogle Scholar
  7. 7.
    Topchy, A., Law, M., Jain, A., Fred, A.: Analysis of Consensus Partition in Cluster Ensemble. In: Fourth IEEE International Conference on Data Mining, pp. 225–232. IEEE Press, New York (2004)CrossRefGoogle Scholar
  8. 8.
    Hadjitodorov, S.T., Kuncheva, L.I., Todorova, L.P.: Moderate diversity for better cluster ensembles. Information Fusion 7(3), 264–275 (2006)CrossRefGoogle Scholar
  9. 9.
    Azimi, J., Fern, X.: Adaptive Cluster Ensemble Selection. In: Proceedings of International Joint Conference on Artificial Intelligence, pp. 992–997 (2009)Google Scholar
  10. 10.
    Kuncheva, L.: Combining Pattern Classifiers. Methods and Algorithms. John Wiley & Sons, Hoboken (2004)CrossRefzbMATHGoogle Scholar
  11. 11.
    Breiman, L.: Random Forests. Machine Learning 45(1), 5–32 (2001)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Vladimir Berikov
    • 1
  1. 1.Sobolev Institute of mathematicsNovosibirsk State UniversityRussia

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