Research Output
Modified cat swarm optimization for clustering
  Clustering is one of the most challenging optimization problems. Many Swarm Intelligence techniques including Ant Colony optimization (ACO), Particle Swarm Optimization (PSO), and Honey Bee Optimization (HBO) have been used to solve clustering. Cat Swarm Optimization (CSO) is one of the newly proposed heuristics in swarm intelligence, which is generated by observing the behavior of cats, and has been used for clustering and numerical function optimization. CSO based clustering is dependent on a pre-specified value of K i.e. Number of Clusters. In this paper we have proposed a “Modified Cat Swam Optimization (MCSO)” heuristic to discover clusters based on the nature of data rather than user specified K. MCSO performs a data scan to determine the initial cluster centers. We have compared the results of MCSO with CSO to demonstrate the enhanced efficiency and accuracy of our proposed technique.

  • Date:

    13 November 2016

  • Publication Status:

    Published

  • DOI:

    10.1007/978-3-319-49685-6_15

  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    004 Data processing & computer science

  • Funders:

    Historic Funder (pre-Worktribe)

Citation

Razzaq, S., Maqbool, F., & Hussain, A. (2016). Modified cat swarm optimization for clustering. In Advances in Brain Inspired Cognitive Systems 8th International Conference, BICS 2016, Beijing, China, November 28-30, 2016, Proceedings, (161-170). https://doi.org/10.1007/978-3-319-49685-6_15

Authors

Keywords

Clustering; Cat Swarm Optimization; Swarm Intelligence

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