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A Probabilistic Model of Fuzzy Clustering Ensemble

  • Mathematical Method in Pattern Recognition
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Abstract

A probabilistic model of clustering ensemble based on a collection of fuzzy clustering algorithms and a weighted co-association matrix is proposed. An expression for the upper bound of the misclassification probability of an arbitrary pair of objects is obtained depending on the characteristics of the ensemble. This expression is used to determine the optimal weights of the algorithms.

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Authors and Affiliations

  1. Sobolev Institute of Mathematics, Siberian Branch, Russian Academy of Sciences, Novosibirsk, 630090, Russia

    V. B. Berikov

  2. Novosibirsk State University, Novosibirsk, 630090, Russia

    V. B. Berikov

Authors
  1. V. B. Berikov
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Correspondence to V. B. Berikov.

Additional information

Vladimir Borisovich Berikov. Born 1964. Graduated from Novosibirsk State University in 1986. Received candidate’s degree in 1996 and doctoral degree in 2007. Scientific interests: mathematical methods of data analysis and their application in the field of image processing and medical data. Author of one monograph and 56 papers. Recognized by MAIK Nauka/Interperiodica for a cycle of studies devoted to the theory and methods of constructing recognition decision functions based on the analysis of empirical information. Member of the Association for Pattern Recognition and Image Analysis of the Russian Federation, the ITHEA International Scientific Society, and the Institute of Electrical and Electronics Engineers (IEEE).

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Berikov, V.B. A Probabilistic Model of Fuzzy Clustering Ensemble. Pattern Recognit. Image Anal. 28, 1–10 (2018). https://doi.org/10.1134/S1054661818010029

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  • DOI: https://doi.org/10.1134/S1054661818010029

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