Abstract
Fuzzy clustering provides a (fuzzy) classification of data into different classes. From the result of a fuzzy cluster analysis fuzzy classification rules can be derived. The most common techniques for this derivation of rules are based on projections of the clusters. The corresponding rules classify only approximately in the same way as the fuzzy clusters themselves, since a certain loss of information has to be tolerated caused by the projections. In this paper, we propose to compute the class or cluster boundaries induced by the fuzzy clusters explicitly and to build up fuzzy rules that reflect exactly these boundaries.
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von Schmidt, B., Klawonn, F. (2003). Extracting Fuzzy Classification Rules from Fuzzy Clusters on the Basis of Separating Hyperplanes. In: Casillas, J., Cordón, O., Herrera, F., Magdalena, L. (eds) Interpretability Issues in Fuzzy Modeling. Studies in Fuzziness and Soft Computing, vol 128. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-37057-4_27
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DOI: https://doi.org/10.1007/978-3-540-37057-4_27
Publisher Name: Springer, Berlin, Heidelberg
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