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Cooperation in Classification and Data Analysis pp 71–80Cite as

Fuzzy Clustering Based Regression with Attribute Weights

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Abstract

Fuzzy regression methods are proposed considering classification structure which is obtained as a result of fuzzy clustering with respect to each attribute. The fuzzy clustering is based on dissimilarities over objects in the subspace of the object’s space. Exploiting the degree of belongingness of objects to clusters with respect to attributes, we define two fuzzy regression methods in order to estimate the fuzzy cluster loadings and weighted regression coefficients. Numerical examples show the applicability of our proposed method

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Authors and Affiliations

  1. Department of Risk Engineering, University of Tsukuba, Tennodai 1-1-1, Tsukuba, Ibaraki, 305-8573, Japan

    M. Sato-Ilic (Faculty of Systems and Information Engineering)

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  1. M. Sato-Ilic
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Correspondence to M. Sato-Ilic .

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    © 2009 Springer-Verlag Berlin Heidelberg

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    Sato-Ilic, M. (2009). Fuzzy Clustering Based Regression with Attribute Weights. In: Gaul, W., Bock, HH., Imaizumi, T., Okada, A. (eds) Cooperation in Classification and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00668-5_7

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