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K-Means Initialization Methods for Improving Clustering by Simulated Annealing

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Advances in Artificial Intelligence – IBERAMIA 2008 (IBERAMIA 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5290))

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Abstract

Clustering is defined as the task of dividing a data set such that elements within each subset are similar between themselves and are dissimilar to elements belonging to other subsets. This problem can be understood as an optimization problem that looks for the best configuration of the clusters among all possible configurations. K-means is the most popular approximate algorithm applied to the clustering problem, but it is very sensitive to the start solution and can get stuck in local optima. Metaheuristics can also be used to solve the problem. Nevertheless, the direct application of metaheuristics to the clustering problem seems to be effective only on small data sets. This work suggests the use of methods for finding initial solutions to the K-means algorithm in order to initialize Simulated Annealing and search solutions near the global optima.

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Authors and Affiliations

  1. Universidade Federal do Espírito Santo, Departamento de Informática, Av. Fernando Ferrari, s/n, Campus de Goiabeiras, CEP 29060-900, Vitória-ES, Brasil

    Gabriela Trazzi Perim, Estefhan Dazzi Wandekokem & Flávio Miguel Varejão

Authors
  1. Gabriela Trazzi Perim
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  2. Estefhan Dazzi Wandekokem
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  3. Flávio Miguel Varejão
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Editor information

Editors and Affiliations

  1. ICREA & Universitat Pompeu Fabra, Paseo de Circumvalacion 8, 08003, Barcelona, Spain

    Hector Geffner

  2. IST-UTL and INESC-ID, Av. Prof. Cavaco Silva - Taguspark, 2744-016, Porto Salvo, Portugal

    Rui Prada

  3. ADETTI/ISCTE and ISCTE, Lisbon University Institute, Av. das Forças Armadas, 1649-026, Lisbon, Portugal

    Isabel Machado Alexandre

  4. ADETTI/ISCTE and ISCTE, Lisbon University Institute, , Av. das Forças Armadas, 1649-026, Lisbon, Portugal

    Nuno David

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© 2008 Springer-Verlag Berlin Heidelberg

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Perim, G.T., Wandekokem, E.D., Varejão, F.M. (2008). K-Means Initialization Methods for Improving Clustering by Simulated Annealing. In: Geffner, H., Prada, R., Machado Alexandre, I., David, N. (eds) Advances in Artificial Intelligence – IBERAMIA 2008. IBERAMIA 2008. Lecture Notes in Computer Science(), vol 5290. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88309-8_14

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  • DOI: https://doi.org/10.1007/978-3-540-88309-8_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-88308-1

  • Online ISBN: 978-3-540-88309-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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