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Differential Evolution with Competing Strategies Applied to Partitional Clustering

  • Conference paper
Swarm and Evolutionary Computation (EC 2012, SIDE 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7269))

Abstract

We consider the problem of optimal partitional clustering of real data sets by optimizing three basic criteria (trace of within scatter matrix, variance ratio criterion, and Marriottt’s criterion). Four variants of the algorithm based on differential evolution with competing strategies are compared on eight real-world data sets. The experimental results showed that hybrid variants with k-means algorithm for a local search are essentially more efficient than the others. However, the use of Marriottt’s criterion resulted in stopping hybrid variants at a local minimum.

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Author information

Authors and Affiliations

  1. Institute for Research and Applications of Fuzzy Modeling, Centre of Excellence IT4Innovations, Division of University of Ostrava, 30. dubna Street 22, 701 03, Ostrava, Czech Republic

    Josef Tvrdík & Ivan Křivý

Authors
  1. Josef Tvrdík
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  2. Ivan Křivý
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Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski , Marcin Korytkowski  & Rafał Scherer ,  & 

  2. AGH University of Science and Technology, Mickiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Department of Electrical Engineering and Computer Sciences, Computer Science Division, University of California Berkeley, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Computational Intelligence Laboratory, Electrical and Computer Engineering, University of Louisville, 405 Lutz Hallz, 40292, Louisville, KY, USA

    Jacek M. Zurada

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© 2012 Springer-Verlag Berlin Heidelberg

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Tvrdík, J., Křivý, I. (2012). Differential Evolution with Competing Strategies Applied to Partitional Clustering. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Swarm and Evolutionary Computation. EC SIDE 2012 2012. Lecture Notes in Computer Science, vol 7269. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29353-5_16

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  • DOI: https://doi.org/10.1007/978-3-642-29353-5_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29352-8

  • Online ISBN: 978-3-642-29353-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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