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Fuzzy Sets in Information Retrieval and Cluster Analysis

  • Book
  • © 1990

Overview

Authors:
  1. Sadaaki Miyamoto

    1. University of Tsukuba, Japan

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Part of the book series: Theory and Decision Library D: (TDLD, volume 4)

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Table of contents (9 chapters)

  1. Front Matter

    Pages i-x
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  2. Introduction

    • Sadaaki Miyamoto
    Pages 1-6

  3. Fuzzy Sets

    • Sadaaki Miyamoto
    Pages 7-44

  4. Review of Information Retrieval

    • Sadaaki Miyamoto
    Pages 45-68

  5. Introduction to Fuzzy Information Retrieval

    • Sadaaki Miyamoto
    Pages 69-81

  6. Information Retrieval Through Fuzzy Associations

    • Sadaaki Miyamoto
    Pages 83-123

  7. Hierarchical Cluster Analysis and Fuzzy Sets

    • Sadaaki Miyamoto
    Pages 125-188

  8. Feedback in Information Retrieval and Search for Clusters

    • Sadaaki Miyamoto
    Pages 189-204

  9. Other Methods in Fuzzy Information Retrieval and Related Topics

    • Sadaaki Miyamoto
    Pages 205-237

  10. Discussion and Suggestions for Further Studies

    • Sadaaki Miyamoto
    Pages 239-242

  11. Back Matter

    Pages 243-261
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Keywords

About this book

The present monograph intends to establish a solid link among three fields: fuzzy set theory, information retrieval, and cluster analysis. Fuzzy set theory supplies new concepts and methods for the other two fields, and provides a common frame­ work within which they can be reorganized. Four principal groups of readers are assumed: researchers or students who are interested in (a) application of fuzzy sets, (b) theory of information retrieval or bibliographic databases, (c) hierarchical clustering, and (d) application of methods in systems science. Readers in group (a) may notice that the fuzzy set theory used here is very simple, since only finite sets are dealt with. This simplification enables the max­ min algebra to deal with fuzzy relations and matrices as equivalent entities. Fuzzy graphs are also used for describing theoretical properties of fuzzy relations. This assumption of finite sets is sufficient for applying fuzzy sets to information retrieval and cluster analysis. This means that little theory, beyond the basic theory of fuzzy sets, is required. Although readers in group (b) with little background in the theory of fuzzy sets may have difficulty with a few sections, they will also find enough in this monograph to support an intuitive grasp of this new concept of fuzzy information retrieval. Chapter 4 provides fuzzy retrieval without the use of mathematical symbols. Also, fuzzy graphs will serve as an aid to the intuitive understanding of fuzzy relations.

Authors and Affiliations

  • University of Tsukuba, Japan

    Sadaaki Miyamoto

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