EGMM: An evidential version of the Gaussian mixture model for clustering - Jiao, Lianmeng; Denœux, Thierry; Liu, Zhun-ga; Pan, Quan (Applied soft computing, 2022)
The Gaussian mixture model (GMM) provides a simple 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 simple as the classical GMM, but can generate a more informative evidential partition for the considered dataset. The synthetic and real dataset experiments show that the proposed EGMM performs better than other representative clustering algorithms. Besides, its superiority is also demonstrated by an application to multi-modal brain image segmentation. •An evidential Gaussian mixture model is proposed to characterize cluster uncertainty.•Model parameters are estimated by Expectation-Maximization algorithm.•A validity index allowing determination of the number of clusters is provided.•Experiments demonstrate the better performance of the proposed method.•An application to multi-modal brain image segmentation is exploited.