Abstract
Clustering is a core problem in data mining and machine learning though it is widely applied in many fields. Recently, it is very popular to use the evolutionary algorithm to solve the problem. This paper proposes an automatic clustering differential evolution (DE) technique for the problem. This approach can be characterized by (i) proposing a modified point symmetry-based cluster validity index (CVI) as a measure of the validity of the corresponding partitioning, (ii) using the Kd-tree based nearest neighbor search to reduce the complexity of finding the closest symmetric point, and (iii) employing a new representation to represent an individual. Experiments conducted on 6 artificial data sets of diverse complexities indicate that this approach is suitable for both the symmetrical intra-clusters and the symmetrical inter-clusters. In addition, it is able to find the optimal number of clusters of the data. Furthermore, based on the comparison with the original point symmetry-based CVI, this proposed point symmetry-based CVI shows better performance in terms of the F-measure and the number of clusters found.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31(3), 264–323 (1999)
Omran, M.G.H., Engelbrecht, A.P., Salman, A.: An overview of clustering methods. Intell. Data Anal. 11(6), 583–605 (2007)
Frigui, H., Krishnapuram, R.: A robust competitive clustering algorithm with applications in computer vision. IEEE Trans. Pattern Anal. Mach. Intell. 21(5), 450–465 (1999)
Leung, Y., Zhang, J.S., Xu, Z.B.: Clustering by scale-space filtering. IEEE Trans. Pattern Anal. Mach. Intell. 22(12), 1396–1410 (2000)
Bandyopadhyay, S., Maulik, U.: Genetic clustering for automatic evolution of clusters and application to image classification. Pattern Recognition 35(6), 1197–1208 (2002)
Sheng, W., Swift, S., Zhang, L., Liu, X.: A weighted sum validity function for clustering with a hybrid niching genetic algorithm. IEEE Transactions on Systems, Man, and Cybernetics, Part B 35(6), 1156–1167 (2005)
Das, S., Abraham, A., Konar, A.: Automatic clustering using an improved differential evolution algorithm. IEEE Transaction on Systems Man and Cybernetics: Part A 38(1), 218–237 (2008)
Bandyopadhyay, S., Saha, S.: A point symmetry-based clustering technique for automatic evolution of clusters. IEEE Trans. on Knowl. and Data Eng. 20(11), 1441–1457 (2008)
Pakhira, M.K., Bandyopadhyay, S., Maulik, U.: Validity index for crisp and fuzzy clusters. Pattern Recognition 37(3), 487–501 (2004)
Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11(4), 341–359 (1997)
Price, K., Storn, R., Lampinen, J.: Differential Evolution: A Practical Approach to Global Optimization. Springer, Berlin (2005)
Chakraborty, U.: Advances in Differential Evolution. Springer, Berlin (2008)
Paterlini, S., Krink, T.: High performance clustering with differential evolution. In: Proceedings of 2004 Congress on Evolutionary Computation, pp. 2004–2011. IEEE Press, Los Alamitos (2004)
Paterlini, S., Krink, T.: Differential evolution and particle swarm optimisation in partitional clustering. Comput. Stat. Data Anal. 50, 1220–1247 (2006)
Alatas, B., Akin, E., Karci, A.: Modenar: Multi-objective differential evolution algorithm for mining numeric association rules. Applied Soft Computing 8(1), 646–656 (2008)
Storn, R., Price, K.: Home page of differential evolution (2008), http://www.icsi.berkeley.edu/~storn/code.html
Su, M.C., Chou, C.H.: A modified version of the k-means algorithm with a distance based on cluster symmetry. IEEE Trans. Pattern Anal. Mach. Intell. 23(6), 674–680 (2001)
Chung, K.L., Lin, J.S.: Faster and more robust point symmetry-based k-means algorithm. Pattern Recogn. 40(2), 410–422 (2007)
Bandyopadhyay, S., Saha, S.: Gaps: A clustering method using a new point symmetry-based distance measure. Pattern Recogn. 40(12), 3430–3451 (2007)
Mount, D., Arya, S.: Ann: A library for approximate nearest neighbor searching (2008), http://www.cs.umd.edu/~mount/ann
van Rijsbergen, C.: Information Retrieval, 2nd edn. Butterworths, London (1979)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gong, W., Cai, Z., Ling, C.X., Huang, B. (2009). A Point Symmetry-Based Automatic Clustering Approach Using Differential Evolution. In: Cai, Z., Li, Z., Kang, Z., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2009. Lecture Notes in Computer Science, vol 5821. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04843-2_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-04843-2_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04842-5
Online ISBN: 978-3-642-04843-2
eBook Packages: Computer ScienceComputer Science (R0)