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Integrating and Extending Fuzzy Clustering and Inferencing to Improve Text Retrieval Performance

  • Donald H. Kraft
  • Jianhua Chen
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 7)

Abstract

We present an integrated approach to information retrieval, which combines fuzzy clustering and fuzzy inference in order to improve textual retrieval performance. We capture the relationships among index terms by using fuzzy logic rules (with truth value assignment in [0,1]). We adapt fuzzy clustering methods (e.g., fuzzy c-means and fuzzy hierarchical clustering) in order to cluster documents with respect to the terms. The clusters generated provide a basis for building fuzzy logic rules concerning the terms, and the clusters can also be used to form hyperlinks between documents. The fuzzy logic rules are applied via fuzzy inference in order to derive query modification. In addition, relevance feedback is discussed as an alternative way to employ the fuzzy clusters. We explore retrieving an entire fuzzy cluster in response to a query. Finally, we note the need to test this approach more thoroughly on a larger standard test bed.

Keywords

Fuzzy Rule Cluster Center Fuzzy Cluster Relevance Feedback Retrieval Performance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Donald H. Kraft
    • 1
  • Jianhua Chen
    • 1
  1. 1.Department of Computer ScienceLouisiana State UniversityBaton RougeUSA

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