Possibilistic Clustering Based on Robust Modeling of Finite Generalized Dirichlet Mixture

We propose a novel possibilistic clustering algorithm based on robust modelling of the Generalized Dirichlet (GD) finite mixture. The algorithm generates two types of membership degrees. The first one is a posterior probability that indicates the degree to which the point fits the estimated distribution. The second membership represents the degree of “typicality” and is used to indentify and discard noise points. The algorithm minimizes one objective function to optimize GD mixture parameters and possibilistic membership values. This optimization is done iteratively by dynamically updating the Dirichlet mixture parameters and the membership values in each iteration. We compare the performance of the proposed algorithm with an EM based approach. We show that the possibilistic approach is more robust.