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.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
L. A. Zadeh, “Similarity relations and fuzzy ordering,” Information Sciences, 3, 177–200 (1971).
S. Tamura, S. Higuchi, and K. Tanaka, “Pattern classification based on fuzzy relations,” IEEE Trans. Systems, Man and Cybernetics, SMC-1, 61–66 (1971).
M.-S. Yang and H.-M. Shih, “Cluster analysis based on fuzzy relations,” Fuzzy Sets and Systems, 120, 197–212 (2001).
A. A. Barsegyan, M. S. Kupriyanov, V. V. Stepanenko, et al., “Methods and models of data analysis: OLAP and Data Mining,” BHV-Petersburg, St. Petersburg (2004).
A. Kofman, Introduction to Fuzzy Set Theory [Russian translation], Radio i Svyaz’, Moscow (1982).
E. H. Ruspini, “Recent developments in fuzzy cluster analysis,” in: Fuzzy Sets and Possibility Theory: Recent Developments, Radio i Svyaz’, Moscow (1986), pp. 114–132.
D. A. Pospelov (ed.), Fuzzy Sets in Models of Control and Artificial Intelligence [in Russian], Nauka, Moscow (1986).
Translated from Kibernetika i Sistemnyi Analiz, No. 1, January–February, 2016, pp. 34–41.
About this article
Cite this article
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
- fuzzy cluster
- cluster analysis