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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Jain, A., Dubes, R.: Algorithms for Clustering Data. Prentice Hall, Upper Saddle River (1988)
Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31(3), 264–323 (1999)
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)
SanJuan, E., Ibekwe-SanJuan, F.: Text mining without document context. Inf. Process. Manage. 42(6), 1532–1552 (2006)
Perdisci, R., Giacinto, G., Roli, F.: Alarm clustering for intrusion detection systems in computer networks. Eng. Appl. Artif. Intell. 19(4), 429–438 (2006)
Jaenichen, S., Perneri, P.: Acquisition of concept descriptions by conceptual clustering (2005)
Maulik, U., Bandyopadhyay, S.: Performance evaluation of some clustering algorithms and validity indices. IEEE PAMI 24, 1650–1654 (2002)
Halkidi, M., Batistakis, Y., Vazirgiannis, M.: On clustering validation techniques. J. Intell. Inf. Syst. 17(2), 107–145 (2001)
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)
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)
Vendral, V., Plenio, M.B., Rippin, M.A.: Quantum entanglement. Phys. Rev. Lett. 78(12), 2275–2279 (1997)
Dey, S., Saha, I., Bhattacharyya, S., Maulik, U.: Multi-level thresholding using quantum inspired meta-heuristics. Knowl.-Based Syst. 67, 373–400 (2014)
Mcmohan, D.: Quantum Computing Explained. Wiley, Hoboken (2008)
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)
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)
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)
Han, K.H., Kim, J.H.: Quantum-inspired evolutionary algorithm for a class combinational optimization. IEEE Trans. Evol. Comput. 6(6), 580–593 (2002)
Blum, C., Roli, A.: Metaheuristic in combinatorial optimization: overviewand conceptual comparison. Technical report, IRIDIA, 2001-13
Glover, F., Kochenberger, G.A.: Handbook on Metaheuristics. Kluwer Academic Publishers, New York (2003)
Real life gray scale images, Domain generated in September 2006. Accessed 26 Aug 2017
Benchmark dataset, Page generated Fri Oct 31 12:01:51 2003. Accessed 26 Aug 2017
Kirkpatrik, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 671–680 (1983)
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)
Davies, D., Bouldin, D.: A cluster separation measure. IEEE PAMI 1(2), 224–227 (1979)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Dey, A., Dey, S., Bhattacharyya, S., Snasel, V., Hassanien, A.E. (2018). Simulated Annealing Based Quantum Inspired Automatic Clustering Technique. In: Hassanien, A., Tolba, M., Elhoseny, M., Mostafa, M. (eds) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018). AMLTA 2018. Advances in Intelligent Systems and Computing, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-74690-6_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-74690-6_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-74689-0
Online ISBN: 978-3-319-74690-6
eBook Packages: EngineeringEngineering (R0)