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Artificial bee colony algorithm for clustering: an extreme learning approach

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Abstract

Extreme learning machine (ELM) as a new learning approach has shown its good generalization performance in regression and classification applications. Clustering analysis is an important tool to explore the structure of data and has been employed in many disciplines and applications. In this paper, we present a method that builds on ELM projection of input data into a high-dimensional feature space and followed by unsupervised clustering using artificial bee colony (ABC) algorithm. While ELM projection facilitates separability of clusters, a metaheuristic technique such as ABC algorithm overcomes problems of dependence on initialization of cluster centers and convergence to local minima suffered by conventional algorithms such as K-means. The proposed ELM-ABC algorithm is tested on 12 benchmark data sets. The experimental results show that the ELM-ABC algorithm can effectively improve the quality of clustering.

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Notes

  1. http://www.ics.uci.edu/~mlearn/MLRepository.html.

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Authors and Affiliations

  1. School of Computer and Information Sciences, University of Hyderabad, Hyderabad, 500 046, India

    Abobakr Khalil Alshamiri, Alok Singh & Bapi Raju Surampudi

  2. Cognitive Science Lab, International Institute of Information Technology, Hyderabad, 500 032, India

    Bapi Raju Surampudi

Authors
  1. Abobakr Khalil Alshamiri
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  2. Alok Singh
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  3. Bapi Raju Surampudi
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Corresponding author

Correspondence to Alok Singh.

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Communicated by V. Loia.

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Alshamiri, A.K., Singh, A. & Surampudi, B.R. Artificial bee colony algorithm for clustering: an extreme learning approach. Soft Comput 20, 3163–3176 (2016). https://doi.org/10.1007/s00500-015-1686-5

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  • DOI: https://doi.org/10.1007/s00500-015-1686-5

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