© 1990

Fuzzy Sets in Information Retrieval and Cluster Analysis


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

Table of contents

  1. Front Matter
    Pages i-x
  2. Sadaaki Miyamoto
    Pages 1-6
  3. Sadaaki Miyamoto
    Pages 7-44
  4. Sadaaki Miyamoto
    Pages 45-68
  5. Sadaaki Miyamoto
    Pages 69-81
  6. Sadaaki Miyamoto
    Pages 83-123
  7. Sadaaki Miyamoto
    Pages 125-188
  8. Sadaaki Miyamoto
    Pages 239-242
  9. Back Matter
    Pages 243-261

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.


Algebra algorithm algorithms calculus document fuzzy fuzzy set model system

Authors and affiliations

  1. 1.University of TsukubaJapan

Bibliographic information