Learning-based fuzzy c-means clustering using finite mixtures of scale mixture normal distributions with missing information

One of the most widely used models for data clustering, which has been considered by many researchers in recent years, is finite mixture models. Clustering is generally a process in which each observation is assigned to one of the specified groups. Although the main idea in mixture models is based on normal distribution, but in recent years with the introduction of other distributions of mixture models based on these distributions has been considered by many researchers. In the articles, the EM algorithm and its extensions are used for estimation. However, it is possible that the EM algorithm does not provide good results for clustering because in this method each member of the observation belongs to one class. This limitation led to the use of the fuzzy clustering approach in this type of problem. In this paper is proposed a clustering algorithm‎, ‎based on a fuzzy treatment of finite mixtures‎ of multivariate scale mixture of normal distribution, ‎using Learning-based fuzzy c-means (LB-FCM) algorithm as well as missing data‎. ‎We construct a robust LB-FCM framework for handling missing data assuming the finite mixture of multivariate scale mixture of normal distribution. Comparisons between LB-FCM and EM-type algorithms are made‎. ‎Experimental‎ results and comparisons actually demonstrate the advantage of the proposed LB-FCM‎.