Clustering of Gaussian distributions
Przemysław Spurek, Wieslaw Palka · 2016
Clustering plays a basic role in many areas of data engineering, pattern recognition and image analysis. Gaussian Mixture Model (GMM) and Cross-Entropy Clustering (CEC) can approximate data of varied shapes by covering it with several clusters e.g. elliptical ones. However, it often happens that we need to extract clusters concentrated around lower dimensional non-linear manifolds. Moreover it is problematic to extract a cluster when data contains a big number of components. Here, we propose a method of solving the above problem by clustering density distribution. This approach allows to determine components of various sizes and geometry. Moreover, it is frequent in clustering problems that the data is naturally given by Gaussian distributions.