Abstract
Four methods of c-regression are compared. Two of them are methods of fuzzy clustering: (a) the fuzzy c-regression methods, and (b) an entropy method proposed by the authors. Two others are probabilistic methods of (c) the deterministic annealing, and (d) the mixture distribution method using the EM algorithm. It is shown that the entropy method yields the same formula as that of the deterministic annealing. Clustering results as well as classification functions are compared. The classification functions for fuzzy clustering are fuzzy rules interpolating cluster memberships, while those for the latter two are probabilistic rules. Theoretical properties of the classification functions are studied. A numerical example is shown.
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© 1999 Springer-Verlag Berlin Heidelberg
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Miyamoto, S., Umayahara, K., Nemoto, T. (1999). Four c-Regression Methods and Classification Functions. In: Zhong, N., Skowron, A., Ohsuga, S. (eds) New Directions in Rough Sets, Data Mining, and Granular-Soft Computing. RSFDGrC 1999. Lecture Notes in Computer Science(), vol 1711. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-48061-7_25
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DOI: https://doi.org/10.1007/978-3-540-48061-7_25
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