The Gaussian mixture model (GMM) provides a convenient yet principled
framework for clustering, with properties suitable for statistical inference.
In this paper, we propose a new model-based clustering algorithm, called EGMM
(evidential GMM), in the theoretical framework of belief functions to better
characterize cluster-membership uncertainty. With a mass function representing
the cluster membership of each object, the evidential Gaussian mixture
distribution composed of the components over the powerset of the desired
clusters is proposed to model the entire dataset. The parameters in EGMM are
estimated by a specially designed Expectation-Maximization (EM) algorithm. A
validity index allowing automatic determination of the proper number of
clusters is also provided. The proposed EGMM is as convenient as the classical
GMM, but can generate a more informative evidential partition for the
considered dataset. Experiments with synthetic and real datasets demonstrate
the good performance of the proposed method as compared with some other
prototype-based and model-based clustering techniques