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Black Hole and k-Means Hybrid Clustering Algorithm

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Computational Intelligence in Data Mining

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 990))

Abstract

In recent advancements, to tackle with NP problems, many heuristic approaches have been developed. A lot of meta-heuristic methods are inspired from nature. In this paper, Black Hole optimization approach for data clustering has been hybridized with k-Means to get improved results. As compared to Black Hole optimization approach for data clustering, where the initial population is initialized randomly, in the proposed method, a part of the population is initialized with some of the better results from k-Means clustering algorithm and rest of the population is initialized randomly.

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Acknowledgements

The first author acknowledges his exposure to Gravitational Search Algorithm done in his B. Tech major project with Dr. Santosh Kumar Majhi, at Veer Surendra Sai University of Technology, Burla, Odisha.

Author information

Authors and Affiliations

  1. Department of Computer Science and Technology, Indian Institute of Engineering Science and Technology, Shibpur, Howrah, 711103, India

    Shankho Subhra Pal & Somnath Pal

Authors
  1. Shankho Subhra Pal
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  2. Somnath Pal
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Corresponding author

Correspondence to Shankho Subhra Pal .

Editor information

Editors and Affiliations

  1. Department of Information Technology, Veer Surendra Sai University of Technology, Burla, Sambalpur, Odisha, India

    Himansu Sekhar Behera

  2. Department of Computer Science and Engineering, Sri Sivani College of Engineering, Srikakulam, Andhra Pradesh, India

    Janmenjoy Nayak

  3. Department of Computer Application, Veer Surendra Sai University of Technology, Burla, Sambalpur, Odisha, India

    Bighnaraj Naik

  4. Faculty of Communication Sciences, University of Teramo, Teramo, Italy

    Danilo Pelusi

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Pal, S.S., Pal, S. (2020). Black Hole and k-Means Hybrid Clustering Algorithm. In: Behera, H., Nayak, J., Naik, B., Pelusi, D. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 990. Springer, Singapore. https://doi.org/10.1007/978-981-13-8676-3_35

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