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Automatic Classification Method Based on a Fuzzy Similarity Relation

Abstract

To solve problems of automatic classification, the IFC fuzzy clusterization method is proposed that uses new fuzzy logical operators, namely, threshold triangular norms and conorms. This method differs from clusterization methods based on a fuzzy equivalence relation in that it allows one to develop faster algorithms for constructing clusters. In this case, data on relationships between elements of the set being investigated are not distorted, which provides the transparency of interpretation of the results of investigations. Examples of application of the method to some well-known problems are given.

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Correspondence to L. F. Hulianytskyi.

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Translated from Kibernetika i Sistemnyi Analiz, No. 1, January–February, 2016, pp. 34–41.

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Hulianytskyi, L.F., Riasna, I.I. Automatic Classification Method Based on a Fuzzy Similarity Relation. Cybern Syst Anal 52, 30–37 (2016). https://doi.org/10.1007/s10559-016-9796-3

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Keywords

  • fuzzy cluster
  • classification
  • cluster analysis