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Simulated Annealing Based Quantum Inspired Automatic Clustering Technique

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Book cover The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018) (AMLTA 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((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.

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Author information

Authors and Affiliations

  1. Department of Computer Application, RCC Institute of Information Technology, Beliaghata, Kolkata, 700015, India

    Alokananda Dey & Siddhartha Bhattacharyya

  2. Department of Computer Science and Engineering, OmDayal Group of Institutions, Birshibpur, Howrah, 711316, India

    Sandip Dey

  3. Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Ostrava, Czech Republic

    Vaclav Snasel

  4. Information Technology Department, Faculty of Computers and Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

Authors
  1. Alokananda Dey
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  2. Sandip Dey
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  3. Siddhartha Bhattacharyya
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  4. Vaclav Snasel
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  5. Aboul Ella Hassanien
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Corresponding author

Correspondence to Siddhartha Bhattacharyya .

Editor information

Editors and Affiliations

  1. Faculty of Computers and Information, Information Technology Department, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Faculty of Computers and Information Sciences, Ain Shams University, Cairo, Egypt

    Mohamed F. Tolba

  3. Faculty of Computers and Information, Department of Computer Science and Engineering, Mansoura University, Dakahlia, Egypt

    Mohamed Elhoseny

  4. Arab Academy for Science, Technology and Maritime Transport (AASTMT), Dokki, Egypt

    Mohamed Mostafa

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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

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  • DOI: https://doi.org/10.1007/978-3-319-74690-6_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-74689-0

  • Online ISBN: 978-3-319-74690-6

  • eBook Packages: EngineeringEngineering (R0)

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