The graded possibilistic clustering model
Francesco Masulli, Stefano Rovetta · 2004
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 possibilistic 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.