Clustering web search results using fuzzy ants: Research Articles

  • Authors:

  • Steven Schockaert;Martine De Cock;Chris Cornelis;Etienne E. Kerre

  • Affiliations:

  • Department of Applied Mathematics and Computer Science, Ghent University, Fuzziness and Uncertainty Modelling Research Unit, Krijgslaan 281 (S9), B-9000 Gent, Belgium;Department of Applied Mathematics and Computer Science, Ghent University, Fuzziness and Uncertainty Modelling Research Unit, Krijgslaan 281 (S9), B-9000 Gent, Belgium;Department of Applied Mathematics and Computer Science, Ghent University, Fuzziness and Uncertainty Modelling Research Unit, Krijgslaan 281 (S9), B-9000 Gent, Belgium;Department of Applied Mathematics and Computer Science, Ghent University, Fuzziness and Uncertainty Modelling Research Unit, Krijgslaan 281 (S9), B-9000 Gent, Belgium

  • Venue:
  • International Journal of Intelligent Systems
  • Year:
  • 2007

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Abstract

Algorithms for clustering Web search results have to be efficient and robust. Furthermore they must be able to cluster a data set without using any kind of a priori information, such as the required number of clusters. Clustering algorithms inspired by the behavior of real ants generally meet these requirements. In this article we propose a novel approach to ant-based clustering, based on fuzzy logic. We show that it improves existing approaches and illustrates how our algorithm can be applied to the problem of Web search results clustering. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 455–474, 2007.