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Efficiently finding the optimum number of clusters in a dataset with a new hybrid differential evolution algorithm: DELA

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Abstract

Clustering algorithms, a fundamental base for data mining procedures and learning techniques, suffer from the lack of efficient methods for determining the optimal number of clusters to be found in an arbitrary dataset. The few methods existing in the literature always use some sort of evolutionary algorithm having a cluster validation index as its objective function. In this article, a new evolutionary algorithm, based on a hybrid model of global and local heuristic search, is proposed for the same task, and some experimentation is done with different datasets and indexes. Due to its design, independent of any clustering procedure, it is applicable to virtually any clustering method like the widely used \(k\)-means algorithm. Moreover, the use of non-parametric statistical tests over the experimental results, clearly show the proposed algorithm to be more efficient than other evolutionary algorithms currently used for the same task.

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Acknowledgments

Mexican authors wish to express their gratitude to SIP-IPN, CONACyT and ICyT-DF for their economic support of this research, particularly, through grants SIP-20130932 and ICyT-PICCO-10-113. Spanish Ministry & Economy competitiveness and FEDER contract roadMe (http://roadme.lcc.uma.es): Fundamentals for Real World Applications of Metaheuristics: The Vehicular Network Case TIN2011-28194 (2012–2014).

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Authors and Affiliations

  1. Instituto Politécnico Nacional, Centro de Investigación en Computación (CIC), 07738, Mexico, D.F., Mexico

    Javier Arellano-Verdejo & Salvador Godoy-Calderon

  2. E.T.S.I. Informática, Universidad de Málaga, Boulevard Louis Pasteur s/n, 29071, Málaga, Spain

    Enrique Alba

Authors
  1. Javier Arellano-Verdejo
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  2. Enrique Alba
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  3. Salvador Godoy-Calderon
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Correspondence to Javier Arellano-Verdejo.

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Communicated by V. Loia.

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Arellano-Verdejo, J., Alba, E. & Godoy-Calderon, S. Efficiently finding the optimum number of clusters in a dataset with a new hybrid differential evolution algorithm: DELA. Soft Comput 20, 895–905 (2016). https://doi.org/10.1007/s00500-014-1548-6

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  • DOI: https://doi.org/10.1007/s00500-014-1548-6

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