Cluster validity index based on Jeffrey divergence

Abstract : Cluster validity indexes are very important tools designed for two purposes: comparing the performance of clustering algorithms and determining the number of clusters that best fits the data. These indexes are in general constructed by combining a measure of compactness and a measure of separation. A classical measure of compactness is the variance. As for separation, the distance between cluster centers is used. However, such a distance does not always reflect the quality of the partition between clusters and sometimes gives misleading results. In this paper, we propose a new cluster validity index for which Jeffrey divergence is used to measure separation between clusters. Experimental results are conducted using different types of data and comparison with widely used cluster validity indexes demonstrates the outperformance of the proposed index.
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https://hal-univ-bourgogne.archives-ouvertes.fr/hal-01483608
Contributeur : Le2i - Université de Bourgogne <>
Soumis le : lundi 6 mars 2017 - 11:06:35
Dernière modification le : mercredi 12 septembre 2018 - 01:26:32

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Ahmed Ben Said, Rachid Hadjidj, Sebti Foufou. Cluster validity index based on Jeffrey divergence. Pattern Analysis and Applications, Springer Verlag, 2017, 20 (1), pp.21 - 31. ⟨http://link.springer.com/article/10.1007%2Fs10044-015-0453-7⟩. ⟨10.1007/s10044-015-0453-7⟩. ⟨hal-01483608⟩

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