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The SKM Algorithm: A K-Means Algorithm for Clustering Sequential Data

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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

This paper introduces a new algorithm for clustering sequential data. The SKM algorithm is a K-Means-type algorithm suited for identifying groups of objects with similar trajectories and dynamics. We provide a simulation study to show the good properties of the SKM algorithm. Moreover, a real application to website users’ search patterns shows its usefulness in identifying groups with heterogeneous behavior. We identify two distinct clusters with different styles of website search.

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Author information

Authors and Affiliations

  1. Department of Quantitative Methods, ISCTE Business School and UNIDE, Av. das Forças Armadas, Lisboa, 1649–026, Portugal

    José G. Dias

  2. Department of Quantitative Methods, ISCTE Business School and CIO, Av. das Forças Armadas, Lisboa, 1649–026, Portugal

    Maria João Cortinhal

Authors
  1. José G. Dias
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  2. Maria João Cortinhal
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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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Dias, J.G., Cortinhal, M.J. (2008). The SKM Algorithm: A K-Means Algorithm for Clustering Sequential Data. 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_18

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  • DOI: https://doi.org/10.1007/978-3-540-88309-8_18

  • 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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