General split gaussian Cross-Entropy clustering

Robust mixture models approaches, which use non-normal distributions have recently been upgraded to accommodate asymmetric data. In this article we propose a new method based on the General Split Gaussian distribution (GSG) and Cross-Entropy Clustering (CEC). The GSG is a flexible density with reasonably small number of parameters which are easy to estimate. We combine the model with a clustering method which allows to treat groups separately and estimate parameters individually in each cluster. Consequently, we introduce an effective clustering algorithm which deals with non-normal data.