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A K-means Based Genetic Algorithm for Data Clustering

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International Joint Conference SOCO’16-CISIS’16-ICEUTE’16 (SOCO 2016, CISIS 2016, ICEUTE 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 527))

Abstract

A genetic algorithm, that exploits the K-means principles for dividing objects in groups having high similarity, is proposed. The method evolves a population of chromosomes, each representing a division of objects in a different number of clusters. A group-based crossover, enriched with the one-step K-means operator, and a mutation strategy that reassigns objects to clusters on the base of their distance to the clusters computed so far, allow the approach to determine the best number of groups present in the dataset. The method has been experimented with four different fitness functions on both synthetic and real-world datasets, for which the ground-truth division is known, and compared with the K-means method. Results show that the approach obtains higher values of evaluation indexes than that obtained by the K-means method.

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Acknowledgment

This work has been partially supported by MIUR D.D. n 0001542, under the project \(BA2KNOW - PON03PE\_00001\_1\).

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Authors and Affiliations

  1. National Research Council of Italy (CNR), Institute for High Performance Computing and Networking (ICAR), Via P. Bucci 7/11, 87036, Rende (CS), Italy

    Clara Pizzuti & Nicola Procopio

Authors
  1. Clara Pizzuti
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  2. Nicola Procopio
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Corresponding author

Correspondence to Clara Pizzuti .

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Editors and Affiliations

  1. Computational Intelligence Group, University of the Basque Country (UPV/EHU), San Sebastian, Spain

    Manuel Graña

  2. University of the Basque Country, Vitoria, Spain

    José Manuel López-Guede

  3. Computational Intelligence Group, University of the Basque Country (UPV/EHU), San Sebastian, Spain

    Oier Etxaniz

  4. Department of Civil Engineering, University of Burgos, Burgos, Spain

    Álvaro Herrero

  5. University of Salamanca, Salamanca, Spain

    Héctor Quintián

  6. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Pizzuti, C., Procopio, N. (2017). A K-means Based Genetic Algorithm for Data Clustering. In: Graña, M., López-Guede, J.M., Etxaniz, O., Herrero, Á., Quintián, H., Corchado, E. (eds) International Joint Conference SOCO’16-CISIS’16-ICEUTE’16. SOCO CISIS ICEUTE 2016 2016 2016. Advances in Intelligent Systems and Computing, vol 527. Springer, Cham. https://doi.org/10.1007/978-3-319-47364-2_21

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  • DOI: https://doi.org/10.1007/978-3-319-47364-2_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47363-5

  • Online ISBN: 978-3-319-47364-2

  • eBook Packages: EngineeringEngineering (R0)

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