Clustering and classification of fuzzy data using the fuzzy EM algorithm

Benjamin Quost (1, 2) , Thierry Denoeux (2, 1)

1 Heudiasyc - Heuristique et Diagnostic des Systèmes Complexes [Compiègne]
2 Labex MS2T - Laboratoire d'Excellence "Maîtrise des Systèmes de Systèmes Technologiques"

In this article, we address the problem of clustering imprecise data using a finite mixture of Gaussians. We propose to estimate the parameters of the model using the fuzzy EM algorithm. This extension of the EM algorithm allows us to handle imprecise data represented by fuzzy numbers. First, we briefly recall the principle of the fuzzy EM algorithm. Then, we provide closed-forms for the parameter estimates in the case of Gaussian fuzzy data. We also describe a Monte-Carlo procedure for estimating the parameter updates in the general case. Experiments carried out on synthetic and real data demonstrate the interest of our approach for taking into account attribute and label uncertainty.

Domaines: Intelligence artificielle [cs.AI]

Benjamin Quost, Thierry Denoeux. Clustering and classification of fuzzy data using the fuzzy EM algorithm. Fuzzy Sets and Systems, 2016, 286, pp.134-156. ⟨10.1016/j.fss.2015.04.012⟩. ⟨hal-01294270⟩