Visualization, Clustering and Classification of Multidimensional Astronomical Data
Antonino Staiano, Angelo Ciaramella, Lara De Vinco, Giuseppe O. Longo, Giancarlo Raiconi, Roberto Tagliaferri, Roberto Amato, Carmine Del Mondo, G. Mangano, Gennaro Miele · 2006
Due to the recent technological advances, data mining in massive data sets has evolved as a crucial research field for many if not all areas of research: from astronomy to high energy physics, to genetics etc. In this paper we discuss an implementation of the Probabilistic Principal Surfaces (PPS) which was developed within the framework of the AstroNeural collaboration. PPS are a nonlinear latent variable model which may be regarded as a complete mathematical framework to accomplish some fundamental data mining activities such as: visualization, clustering and classification of high dimensional data. The effectiveness of the proposed model is exemplified referring to a complex astronomical data set.