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A Point Symmetry-Based Automatic Clustering Approach Using Differential Evolution

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Advances in Computation and Intelligence (ISICA 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5821))

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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.

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References

  1. Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31(3), 264–323 (1999)

    Article  Google Scholar 

  2. Omran, M.G.H., Engelbrecht, A.P., Salman, A.: An overview of clustering methods. Intell. Data Anal. 11(6), 583–605 (2007)

    Google Scholar 

  3. 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)

    Article  Google Scholar 

  4. Leung, Y., Zhang, J.S., Xu, Z.B.: Clustering by scale-space filtering. IEEE Trans. Pattern Anal. Mach. Intell. 22(12), 1396–1410 (2000)

    Article  Google Scholar 

  5. Bandyopadhyay, S., Maulik, U.: Genetic clustering for automatic evolution of clusters and application to image classification. Pattern Recognition 35(6), 1197–1208 (2002)

    Article  MATH  Google Scholar 

  6. 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)

    Article  Google Scholar 

  7. 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)

    Article  Google Scholar 

  8. 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)

    Article  Google Scholar 

  9. Pakhira, M.K., Bandyopadhyay, S., Maulik, U.: Validity index for crisp and fuzzy clusters. Pattern Recognition 37(3), 487–501 (2004)

    Article  MATH  Google Scholar 

  10. 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)

    Article  MathSciNet  MATH  Google Scholar 

  11. Price, K., Storn, R., Lampinen, J.: Differential Evolution: A Practical Approach to Global Optimization. Springer, Berlin (2005)

    MATH  Google Scholar 

  12. Chakraborty, U.: Advances in Differential Evolution. Springer, Berlin (2008)

    Book  MATH  Google Scholar 

  13. 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)

    Google Scholar 

  14. Paterlini, S., Krink, T.: Differential evolution and particle swarm optimisation in partitional clustering. Comput. Stat. Data Anal. 50, 1220–1247 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  15. 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)

    Article  Google Scholar 

  16. Storn, R., Price, K.: Home page of differential evolution (2008), http://www.icsi.berkeley.edu/~storn/code.html

  17. 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)

    Article  Google Scholar 

  18. Chung, K.L., Lin, J.S.: Faster and more robust point symmetry-based k-means algorithm. Pattern Recogn. 40(2), 410–422 (2007)

    Article  MATH  Google Scholar 

  19. Bandyopadhyay, S., Saha, S.: Gaps: A clustering method using a new point symmetry-based distance measure. Pattern Recogn. 40(12), 3430–3451 (2007)

    Article  MATH  Google Scholar 

  20. Mount, D., Arya, S.: Ann: A library for approximate nearest neighbor searching (2008), http://www.cs.umd.edu/~mount/ann

  21. van Rijsbergen, C.: Information Retrieval, 2nd edn. Butterworths, London (1979)

    MATH  Google Scholar 

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Authors and Affiliations

  1. School of Computer Science, China University of Geosciences, Wuhan, 430074, P.R. China

    Wenyin Gong, Zhihua Cai & Bo Huang

  2. Department of Computer Science, The University of Western Ontario, London, Canada

    Charles X. Ling

Authors
  1. Wenyin Gong
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  2. Zhihua Cai
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  3. Charles X. Ling
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  4. Bo Huang
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Editor information

Editors and Affiliations

  1. Faculty of Computer Science, China University of Geosciences, 430074, Wuhan, Hubei, P.R. China

    Zhihua Cai

  2. School of Computer Science, China University of Geosciences, 430074, Wuhan, Hubei, China

    Zhenhua Li

  3. Computation Center, Wuhan University, 430072, Wuhan, Hubei, China

    Zhuo Kang

  4. School of Computer Science and Engineering, The University of Aizu, 965-8580, i, Aizu-Wakamatsu City, Fukushima, Japan

    Yong Liu

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© 2009 Springer-Verlag Berlin Heidelberg

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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

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  • 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)

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