Kernel MDL to Determine the Number of Clusters

Ivan O Kyrgyzov,Olexiy O Kyrgyzov,Henri Maître,Marine Campedel

MACHINE LEARNING AND DATA MINING IN PATTERN RECOGNITION, PROCEEDINGS（2007）

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摘要

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.

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关键词

spot5 satellite image,gaussian mixture model,minimum description length,kernel mdl,optimal number,new criterion,synthetic data,k means clustering

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