Abstract
In this paper we propose a new criterion, based on Minimum Description Length (MDL), to estimate an optimal number of clusters. This criterion, called Kernel MDL (KMDL), is particularly adapted to the use of kernel K-means clustering algorithm. Its formulation is based on the definition of MDL derived for Gaussian Mixture Model (GMM). We demonstrate the efficiency of our approach on both synthetic data and real data such as SPOT5 satellite images.
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
Barron, A., Rissanen, J., Yu, B.: The minimum description length principle in coding and modeling. IEEE Trans. Inform. Theory 44(6), 2743–2760 (1998)
Chapelle, O., Vapnik, V., Bousquet, O., Mukherjee, S.: Choosing multiple parameters for support vector machines. Machine Learning 46, 131–159 (2002)
Figueiredo, A.K., Jain, M.A.F.: Unsupervised learning of finite mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(3), 381–396 (2002)
Govaert, G.: Analyse des données. Lavoisier (2003)
Heas, P., Datcu, M.: Modelling trajectory of dynamic clusters in image time-series for spatio-temporal reasoning. IEEE Transactions on Geoscience and Remote Sensing 43(7), 1635–1647 (2005)
Maître, H., Kyrgyzov, I., Campedel, M.: Combining clustering results for the analysis of textures of spot5 images. In: ESA-EUSC: Image Information Mining (2005)
Jain, A., Dubes, R.C.: Algorithms for Clustering Data. Prentice-Hall, Englewood Cliffs, NJ (1988)
Maître, H., Campedel, M., Moulines, E., Datcu, M.: Feature selection for satellite image indexing. In: ESA-EUSC: Image Information Mining (2005)
MacKay, D.J.C.: Information Theory, Inference, and Learning Algorithms. Cambridge University Press, Cambridge (2003)
Rissanen, J.: Modeling by shortest data description. Automatica 14, 465–471 (1978)
Rissanen, J.: Universal coding, information, prediction, and estimation. IEEE Trans. Inform. Theory 30(4), 629–636 (1984)
Scholkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge, MA, USA (2001)
Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kyrgyzov, I.O., Kyrgyzov, O.O., Maître, H., Campedel, M. (2007). Kernel MDL to Determine the Number of Clusters. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2007. Lecture Notes in Computer Science(), vol 4571. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73499-4_16
Download citation
DOI: https://doi.org/10.1007/978-3-540-73499-4_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73498-7
Online ISBN: 978-3-540-73499-4
eBook Packages: Computer ScienceComputer Science (R0)