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Proceedings of the Second International Conference on Computer and Communication Technologies pp 343–352Cite as

Evolutionary Improved Swarm-Based Hybrid K-Means Algorithm for Cluster Analysis

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 379))

Abstract

Improvement in the quality of cluster centers and minimization of intra-cluster distance are two most challenging areas of K-means clustering algorithm. Due to predetermined number of clusters, it is difficult to predict the exact value of k. Furthermore, in case of non-globular clusters, K-means fails to get optimal cluster center in a data set. In this paper, a hybrid improved particle swarm optimization-based evolutionary K-means clustering method has been proposed to obtain the optimal cluster center. The hybridization of improved PSO and genetic algorithm (GA) along with K-means algorithm improves the convergence speed as well as helps to find the global optimal solution. In the first stage, IPSO has been used to get a global solution in order to get optimal cluster centers. Then, the crossover steps of GA are used to improve the quality of particles and mutation is used for diversification of solution space in order to avoid premature convergence. The performance analysis of the proposed method is compared with other existing clustering techniques like K-means, GA-K-means, and PSO-K-means.

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Acknowledgments

This work is supported by the Department of Science & Technology (DST), Ministry of Science & Technology, New Delhi, Govt. of India, under grants No. DST/INSPIRE Fellowship/2013/585.

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Authors and Affiliations

  1. Department of CSE and IT, Veer Surendra Sai University of Technology, Burla, 768018, Odisha, India

    Janmenjoy Nayak, D. P. Kanungo, Bighnaraj Naik & H. S. Behera

Authors
  1. Janmenjoy Nayak
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  2. D. P. Kanungo
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  3. Bighnaraj Naik
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  4. H. S. Behera
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Corresponding author

Correspondence to Janmenjoy Nayak .

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Editors and Affiliations

  1. Dept. of Computer Science and Engineering, Anil Neerukonda Institute of Technology and Sciences, Visakhapatnam, India

    Suresh Chandra Satapathy

  2. Department of CSE, CMR Technical Campus, Hyderabad, India

    K. Srujan Raju

  3. Computer Science & Engineering, Kalyani University, Nadia, West Bengal, India

    Jyotsna Kumar Mandal

  4. Electronics and Communication Engineering, Shri Ramswaroop Memorial Group of Professional Colleges, Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

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Nayak, J., Kanungo, D.P., Naik, B., Behera, H.S. (2016). Evolutionary Improved Swarm-Based Hybrid K-Means Algorithm for Cluster Analysis. In: Satapathy, S., Raju, K., Mandal, J., Bhateja, V. (eds) Proceedings of the Second International Conference on Computer and Communication Technologies. Advances in Intelligent Systems and Computing, vol 379. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2517-1_34

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  • DOI: https://doi.org/10.1007/978-81-322-2517-1_34

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2516-4

  • Online ISBN: 978-81-322-2517-1

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