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Simulated Annealing Based Quantum Inspired Automatic Clustering Technique

  • Alokananda Dey
  • Sandip Dey
  • Siddhartha Bhattacharyya
  • Vaclav Snasel
  • Aboul Ella Hassanien
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 723)

Abstract

Cluster analysis is a popular technique whose aim is to segregate a set of data points into groups, called clusters. Simulated Annealing (SA) is a popular meta-heuristic inspired by the annealing process used in metallurgy, useful in solving complex optimization problems. In this paper, the use of the Quantum Computing (QC) and SA is explored to design Quantum Inspired Simulated Annealing technique, which can be applied to compute optimum number of clusters for image clustering. Experimental results over a number of images endorse the effectiveness of the proposed technique pertaining to fitness value, convergence time, accuracy, robustness, and standard error. The paper also reports the computation results of a statistical superiority test, known as t-test. An experimental judgement to the classical technique has also be presented, which eventually demonstrates that the proposed technique outperforms the other.

Keywords

Simulated annealing Automatic clustering Cluster validity Quantum computing 

References

  1. 1.
    Jain, A., Dubes, R.: Algorithms for Clustering Data. Prentice Hall, Upper Saddle River (1988)zbMATHGoogle Scholar
  2. 2.
    Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31(3), 264–323 (1999)CrossRefGoogle Scholar
  3. 3.
    Chou, C.H., Su, M.C., La, E.: A new cluster validity measure and its application to image compression. Pattern Anal. Appl. 7(2), 205–250 (2004)MathSciNetCrossRefGoogle Scholar
  4. 4.
    SanJuan, E., Ibekwe-SanJuan, F.: Text mining without document context. Inf. Process. Manage. 42(6), 1532–1552 (2006)CrossRefGoogle Scholar
  5. 5.
    Perdisci, R., Giacinto, G., Roli, F.: Alarm clustering for intrusion detection systems in computer networks. Eng. Appl. Artif. Intell. 19(4), 429–438 (2006)CrossRefGoogle Scholar
  6. 6.
    Jaenichen, S., Perneri, P.: Acquisition of concept descriptions by conceptual clustering (2005)Google Scholar
  7. 7.
    Maulik, U., Bandyopadhyay, S.: Performance evaluation of some clustering algorithms and validity indices. IEEE PAMI 24, 1650–1654 (2002)CrossRefGoogle Scholar
  8. 8.
    Halkidi, M., Batistakis, Y., Vazirgiannis, M.: On clustering validation techniques. J. Intell. Inf. Syst. 17(2), 107–145 (2001)CrossRefzbMATHGoogle Scholar
  9. 9.
    Dey, S., Bhattacharyya, S., Maulik, U.: Quantum inspired genetic algorithm and particle swarm optimization using chaotic map model based interference for gray level image thresholding. Swarm Evol. Comput. 15, 38–57 (2014)CrossRefGoogle Scholar
  10. 10.
    Dey, S., Bhattacharyya, S., Maulik, U.: Efficient quantum inspired meta-heuristics for multi-level true colour image thresholding. Appl. Soft Comput. 56, 472–513 (2017)CrossRefGoogle Scholar
  11. 11.
    Vendral, V., Plenio, M.B., Rippin, M.A.: Quantum entanglement. Phys. Rev. Lett. 78(12), 2275–2279 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Dey, S., Saha, I., Bhattacharyya, S., Maulik, U.: Multi-level thresholding using quantum inspired meta-heuristics. Knowl.-Based Syst. 67, 373–400 (2014)CrossRefGoogle Scholar
  13. 13.
    Mcmohan, D.: Quantum Computing Explained. Wiley, Hoboken (2008)Google Scholar
  14. 14.
    Dey, S., Bhattacharyya, S., Maulik, U.: New quantum inspired meta-heuristic techniques for multi-level colour image thresholding. Appl. Soft Comput. 46, 677–702 (2016)CrossRefGoogle Scholar
  15. 15.
    Dey, S., Bhattacharyya, S., Maullik, U.: Quantum behaved swarm intelligent techniques for image analysis: a detailed survey. In: Bhattacharyya, S., Dutta, P. (eds.) Handbook of Research on Swarm Intelligence in Engineering. IGI Global, Hershey (2015)Google Scholar
  16. 16.
    Dey, S., Bhattacharyya, S., Maullik, U.: Optimum gray level image thresholding using a quantum inspired genetic algorithm. In: Advanced Research on Hybrid Intelligent Techniques and Applications (2015)Google Scholar
  17. 17.
    Han, K.H., Kim, J.H.: Quantum-inspired evolutionary algorithm for a class combinational optimization. IEEE Trans. Evol. Comput. 6(6), 580–593 (2002)CrossRefGoogle Scholar
  18. 18.
    Blum, C., Roli, A.: Metaheuristic in combinatorial optimization: overviewand conceptual comparison. Technical report, IRIDIA, 2001-13Google Scholar
  19. 19.
    Glover, F., Kochenberger, G.A.: Handbook on Metaheuristics. Kluwer Academic Publishers, New York (2003)CrossRefzbMATHGoogle Scholar
  20. 20.
    Real life gray scale images, Domain generated in September 2006. Accessed 26 Aug 2017Google Scholar
  21. 21.
    Benchmark dataset, Page generated Fri Oct 31 12:01:51 2003. Accessed 26 Aug 2017Google Scholar
  22. 22.
    Kirkpatrik, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 671–680 (1983)MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Dey, S., Bhattacharyya, S., Maulik, U.: Chaotic map model based interference employed in quantum inspired genetic algorithm to determine the optimum gray level image thresholding. In: Global Trends in Intelligent Computing Research and Development, pp. 68–110 (2013)Google Scholar
  24. 24.
    Davies, D., Bouldin, D.: A cluster separation measure. IEEE PAMI 1(2), 224–227 (1979)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Computer ApplicationRCC Institute of Information TechnologyKolkataIndia
  2. 2.Department of Computer Science and EngineeringOmDayal Group of InstitutionsHowrahIndia
  3. 3.Faculty of Electrical Engineering and Computer ScienceVSB-Technical University of OstravaOstravaCzech Republic
  4. 4.Information Technology Department, Faculty of Computers and InformationCairo UniversityGizaEgypt

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