Cluster validity index based on Jeffrey divergence
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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.
KeywordsClustering Cluster validity index Jeffrey divergence
This publication was made possible by NPRP Grant # 4-1165- 2-453 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.
- 2.Athanasios P (1991) Probability, random variables and stochastic processes, 3rd edn. McGraw-Hill Companies, New YorkGoogle Scholar
- 3.Bache K, Lichman M (2013) UCI machine learning repository. http://archive.ics.uci.edu/ml
- 11.Ester M, Peter Kriegel H, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. AAAI Press, Palo AltoGoogle Scholar
- 12.Everitt BS, Landau S, Leese M, Stahl D (2011) An introduction to classification and clustering, chap 1:1–13. Wiley, New YorkGoogle Scholar
- 15.Goldberger J, Hinton GE, Roweis ST, Salakhutdinov R (2005) Neighbourhood components analysis. In: Saul L, Weiss Y, Bottou L (eds) Advances in neural information processing systems 17. MIT Press, Cambridge, pp 513–520Google Scholar
- 19.Krooshof PW, Postma GJ, Melssen WJ, Buydens LM (2012) Biomedical imaging: principles and applications, chap 12:1–29. Wiley, New YorkGoogle Scholar
- 30.Weinberger KQ, Blitzer J, Saul LK (2006) Distance metric learning for large margin nearest neighbor classification. In. In NIPS, MIT Press, CambridgeGoogle Scholar
- 33.Xing EP, Ng AY, Jordan MI, Russell S (2003) Distance metric learning, with aplicationt o clustering with side-information. In: Advances in neural information processing systems 15, MIT Press, Cambridge, pp 505–512Google Scholar