Fuzzy Clustering with Minkowski Distance Functions

Since Ruspini (1969) first proposed the idea of fuzzy partitions, fuzzy clustering has grown to be an important tool for data analysis and modeling. Especially after the introduction of the fuzzy c-means algorithm (Bezdek, 1973; Dunn, 1973), objective function-based fuzzy clustering has received much attention from the scientific community as well as the practitioners of fuzzy set theory (Baraldi and Blonda, 1999a,b; Bezdek and Pal, 1992; Höppner, Klawonn, Kruse and Runkler, 1999; Yang, 1993). Consequently, fuzzy clustering has been applied extensively for diverse tasks such as pattern recognition (Santoro, Prevete, Cavallo, and Catanzariti, 2006), data analysis (D’Urso, 2005), data mining (Crespo and Weber, 2005), image processing (Yang, Zheng and Lin, 2005), and engineering systems design
(Sheu, 2005). Objective function-based fuzzy clustering has also become one of the key techniques in fuzzy modeling, where it is used for partitioning the feature space from which the rules of a fuzzy system can be derived (Babuska, 1998).
In general [...]