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The VLDB Journal

, Volume 8, Issue 3–4, pp 222–236 | Cite as

Clustering categorical data: an approach based on dynamical systems

  • David Gibson
  • Jon Kleinberg
  • Prabhakar Raghavan
Special section

Abstract.

We describe a novel approach for clustering collections of sets, and its application to the analysis and mining of categorical data. By “categorical data,” we mean tables with fields that cannot be naturally ordered by a metric – e.g., the names of producers of automobiles, or the names of products offered by a manufacturer. Our approach is based on an iterative method for assigning and propagating weights on the categorical values in a table; this facilitates a type of similarity measure arising from the co-occurrence of values in the dataset. Our techniques can be studied analytically in terms of certain types of non-linear dynamical systems.

Key words:Clustering – Data mining – Categorial data – Dynamical systems – Hypergraphs 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • David Gibson
    • 1
  • Jon Kleinberg
    • 2
  • Prabhakar Raghavan
    • 3
  1. 1.Department of Computer Science UC Berkeley, Berkeley, CA 94720 USA; e-mail: dag@cs.berkeley.eduUS
  2. 2.Department of Computer Science, Cornell University, Ithaca, NY 14853; e-mail: kleinber@cs.cornell.eduUS
  3. 3.Almaden Research Center IBM, San Jose, CA 95120 USA; e-mail: pragh@almaden.ibm.comUS

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