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Partitional Algorithms for Hard Clustering Using Evolutionary and Swarm Intelligence Methods: A Survey

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Book cover Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 202))

Abstract

Evolutionary and swarm intelligence methods attracted attention and gained popularity among the data mining researchers due to their expedient implementation, parallel nature, ability to search global optima and other advantages over conventional techniques. These methods along with their variants and hybrid approaches have emerged as worthwhile class of methods for clustering. Clustering is an unsupervised classification method. The partitional clustering algorithms look for hard clustering; they decompose the dataset into a set of disjoint clusters. This paper describes a brief review of evolutionary and swarm intelligence methods with their variants and hybrid approaches designed for partitional clustering algorithms for hard clustering of datasets.

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Authors and Affiliations

  1. Computational Intelligence and Data Mining Research Lab, ABV-Indian Institute of Information Technology and Management, Gwalior, India

    Jay Prakash & Pramod Kumar Singh

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  1. Jay Prakash
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  2. Pramod Kumar Singh
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Correspondence to Jay Prakash .

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Editors and Affiliations

  1. South Asian University, Chankya Puri, New Delhi, 110021, India

    Jagdish Chand Bansal

  2. ABV - IIITM, Gwalior, Gwalior, 474015, Madhya Pradesh, India

    Pramod Singh

  3. , Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, 247667, India

    Kusum Deep

  4. , Department of Paper Technology, Indian Institute of Technology Roorkee, Saharanpur Campus, Roorkee, India

    Millie Pant

  5. , Department of Computer Science, Liverpool Hope University, Office: FML 412, Liverpool, Liverpool, L16 9JD, United Kingdom

    Atulya Nagar

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Prakash, J., Singh, P.K. (2013). Partitional Algorithms for Hard Clustering Using Evolutionary and Swarm Intelligence Methods: A Survey. In: Bansal, J., Singh, P., Deep, K., Pant, M., Nagar, A. (eds) Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012). Advances in Intelligent Systems and Computing, vol 202. Springer, India. https://doi.org/10.1007/978-81-322-1041-2_44

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  • DOI: https://doi.org/10.1007/978-81-322-1041-2_44

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  • Publisher Name: Springer, India

  • Print ISBN: 978-81-322-1040-5

  • Online ISBN: 978-81-322-1041-2

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