Possibilistic and probabilistic fuzzy clustering: unification within the framework of the non-extensive thermostatistics
Fuzzy clustering algorithms are becoming the major technique in cluster analysis. In this paper, we consider the fuzzy clustering based on objective functions. They can be divided into two categories: possibilistic and probabilistic approaches leading to two different function families depending on the conditions required to state that fuzzy clusters are a fuzzy c-partition of the input data. Recently, we have presented in Menard and Eboueya (Fuzzy Sets and Systems, 27, to be published) an axiomatic derivation of the Possibilistic and Maximum Entropy Inference (MEI) clustering approaches, based upon an unifying principle of physics, that of extreme physical information (EPI) defined by Frieden (Physics from Fisher information, A unification, Cambridge University Press, Cambridge, 1999). Here, using the same formalism, we explicitly give a new criterion in order to provide a theoretical justification of the objective functions, constraint terms, membership functions and weighting exponent m used in the probabilistic and possibilistic fuzzy clustering. Moreover, we propose an unified framework including the two procedures. This approach is inspired by the work of Frieden and Plastino and Plastino and Miller (Physics A 235, 577) extending the principle of extremal information in the framework of the non-extensive thermostatistics. Then, we show how, with the help of EPI, one can propose extensions of the FcM and Possibilistic algorithms.
possibilistic and probabilistic fuzzy clustering non-extensive thermostatistics extreme physical information fisher information possibilistic algorithm measures of information fuzzy c-means