Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Jain, A.K.: Data Clustering: 50 Years Beyond K-Means. Pattern Recognition Letters 31(8), 651–666 (2010)
Strehl, A., Ghosh, J.: Clustering ensembles - a knowledge reuse framework for combining multiple partitions. The Journal of Machine Learning Research 3, 583–617 (2002)
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)
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)
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)
Hong, Y., Kwong, S.: To combine steady-state genetic algorithm and ensemble learning for data clustering. Pattern Recognition Letters 29(9), 1416–1423 (2008)
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)
Hadjitodorov, S.T., Kuncheva, L.I., Todorova, L.P.: Moderate diversity for better cluster ensembles. Information Fusion 7(3), 264–275 (2006)
Azimi, J., Fern, X.: Adaptive Cluster Ensemble Selection. In: Proceedings of International Joint Conference on Artificial Intelligence, pp. 992–997 (2009)
Kuncheva, L.: Combining Pattern Classifiers. Methods and Algorithms. John Wiley & Sons, Hoboken (2004)
Breiman, L.: Random Forests. Machine Learning 45(1), 5–32 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Berikov, V. (2011). A Latent Variable Pairwise Classification Model of a Clustering Ensemble. In: Sansone, C., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2011. Lecture Notes in Computer Science, vol 6713. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21557-5_30
Download citation
DOI: https://doi.org/10.1007/978-3-642-21557-5_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21556-8
Online ISBN: 978-3-642-21557-5
eBook Packages: Computer ScienceComputer Science (R0)