The Graded Possibilistic Clustering Model

Francesco Masulli and Stefano Rovetta



Abstract

This paper presents the graded possibilistic model.
After reviewing some clustering algorithms derived from c-
Means, we provide a unified perspective on these clustering
algorithms, focused on the memberships rather than on the cost
function. Then the concept of graded possibility is introduced.
This is a partially possibilistc version of the fuzzy clustering
model, as compared to Krishnapuram and Keller’s possibilistic
clustering. We outline a basic graded possibilistic clustering
algorithm and highlight the different properties attainable by
means of experimental demonstrations.


Proceedings of the 2003 International Joint Conference on Neural networks