Kernel MDL to Determine the Number of Clusters

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.

• Type: Conference
• Year: 2007
• Where: MLDM
• Authors: Ivan O. Kyrgyzov, Olexiy O. Kyrgyzov, Henri Maître, Marine Campedel
• Keywords: K-means Clustering Algorithm, Kernel MDL, Machine Learning, Minimum Description Length, MLDM 2007