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Extending K-Means Clustering to First-Order Representations

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Book cover Inductive Logic Programming (ILP 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1866))

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Abstract

In this paper, we present an in-depth evaluation of two approaches of extending k-means clustering to work on first-order representations. The first-approach, k-medoids, selects its cluster center from the given set of instances, and is thus limited in its choice of centers. The second approach, k-prototypes, uses a heuristic prototype construction algorithm that is capable of generating new centers. The two approaches are empirically evaluated on a standard benchmark problem with respect to clustering quality and convergence. Results show that in this case indeed the k-medoids approach is a viable and fast alternative to existing agglomerative or top-down clustering approaches even for a small-scale dataset, while k-prototypes exhibited a number of deficiencies.

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Author information

Authors and Affiliations

  1. German National Research Center for Information Technology, GMD - AiS.KD, Schloβ Birlinghoven, D-53754, Sankt Augustin

    Mathias Kirsten

  2. IWS, Otto-von-Guericke-Universität Magdeburg, P.O.Box 4120, D-39106, Magdeburg

    Stefan Wrobel

Authors
  1. Mathias Kirsten
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  2. Stefan Wrobel
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of York, Heslington, York, YO10 5DD, UK

    James Cussens  & Alan Frisch  & 

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Kirsten, M., Wrobel, S. (2000). Extending K-Means Clustering to First-Order Representations. In: Cussens, J., Frisch, A. (eds) Inductive Logic Programming. ILP 2000. Lecture Notes in Computer Science(), vol 1866. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44960-4_7

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  • DOI: https://doi.org/10.1007/3-540-44960-4_7

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67795-6

  • Online ISBN: 978-3-540-44960-7

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