Resumen de On the initialization of two-stage clustering with class-GTM
Raúl Cruz Barbosa, Alfredo Vellido Alacena 
Generative Topographic Mapping is a probabilistic model for data clustering and visualization. It maps points, considered as prototype representatives of data clusters, from a low dimensional latent space onto the observed data space. In semi-supervised settings, class information can be added resulting in a model variation called class-GTM. The number of class-GTM latent points used is usually large for visualization purposes and does not necessarily reflect the class structure of the data. It is therefore convenient to group the clusters further in a two-stage procedure. In this paper, class-GTM is first used to obtain the basic cluster prototypes. Two novel methods are proposed to use this information as prior knowledge for the K-means-based second stage. We evaluate, using an entropy measure, whether these methods retain the class separability capabilities of class-GTM in the two-stage process, and whether the two-stage procedure improves on the direct clustering of the data using K-means.
