A Fuzzy Neighborhood Model for Clustering, Classification, and Approximations

  • Sadaaki Miyamoto
  • Satoshi Hayakawa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4259)


A fuzzy neighborhood model for analyzing information systems having topological structures on occurrences of keywords is proposed and algorithms of clustering, classification and approximations similar to generalized rough sets are developed. Real applications include text mining and clustering of keywords on the web. An illustrative example is given.


Text Mining Transitive Closure Agglomerative Hierarchical Cluster Fuzzy Equivalence Relation Natural Distance 
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 2006

Authors and Affiliations

  • Sadaaki Miyamoto
    • 1
  • Satoshi Hayakawa
    • 2
  1. 1.Department of Risk Engineering, School of Systems and Information EngineeringUniversity of TsukubaIbarakiJapan
  2. 2.Graduate School of Systems and Information EngineeringUniversity of TsukubaIbarakiJapan

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