Multi-PFKCN : A fuzzy possibilistic clustering algorithm based on neural network
Balkis Abidi, Sadok Ben Yahia · 2013
The main moan that can addressed to the pioneering approaches of fuzzy clustering stand in their approximate management of a noisy surroundings as well as their snugness dependency of an apriori determination of the number of clusters. The aim of this paper is twofold: First, we introduce a new algorithm, called PFKCN, based on neural network. This algorithm introduces both membership and typicality values, simultaneously, into the Kohonen Network clustering. Then, we tackle the problem of estimating the number of clusters, by using a multi level PFKCN based clustering algorithm, called Multi-PFKCN. The latter is able to find the optimal number of clusters by using a statistical criterion, that aims at measuring the quality of obtained partitions. Carried out experiments on real-life data sets highlights a very encouraging results in terms of exact determination of optimal number of clusters.