An Algorithm to Model Paradigm Shifting in Fuzzy Clustering

Francesco Masulli and Stefano Rovetta



Abstract

The graded possibilistic clustering paradigm includes as the two extreme
cases the “probabilistic” assumption and the “possibilistic” assumption
adopted by many clustering algorithms.We propose an implementation of a graded
possibilistic clustering algorithm based on an interval equality constraint enforcing
both the normality condition and the required graded possibilistic condition.
Experimental results highlight the dierent properties attainable through appropriate
implementation of a suitable graded possibilistic model.



Proceedings of the 2003 Workshop Italiano sulle Reti Neurali