Nerf c-means: Non-Euclidean relational fuzzy clustering

摘要

The relational fuzzy c-means (RFCM) algorithm can be used to cluster a set of n objects described by pair-wise dissimilarity values if (and only if) there exist n points in Rn − 1 whose squared Euclidean distances precisely match the given dissimilarity data. This strong restriction on the dissimilarity data renders RFCM inapplicable to most relational clustering problems. This paper substantially improves RFCM by generalizing it to the case of arbitrary (symmetric) dissimilarity data. The generalization is obtained using a computationally efficient modification of the existing algorithm that is equivalent to applying a “spreading” transformation to the dissimilarity data. While the method given applies specifically to dissimilarity data, a simple transformation can be used to convert similarity relations into dissimilarity data, so the method is applicable to any numerical relational data that are positive, reflexive (or anti-reflexive) and symmetric. Numerical examples illustrate and compare the present approach to problems that can be studied with alternatives such as the linkage algorithms.