Visualization, clustering and classification of multidimensional astronomical data

Author

Staiano, Antonino ; Ciaramella, Angelo ; Vinco, Lara De ; Donalek, Ciro ; Longo, Giuseppe ; Raiconi, Giancarlo ; Tagliaferri, Roberto ; Amato, Roberto ; Mondo, Carmine Del ; Mangano, Giuseppe ; Miele, Gennaro

Author_Institution

Dipt. di Matematica ed Informatica, Salerno Univ., Fisciano, Italy

fYear

2005

fDate

4-6 July 2005

Firstpage

141

Lastpage

146

Abstract

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.

Keywords

astronomy computing; data mining; data visualisation; pattern classification; pattern clustering; probability; very large databases; AstroNeural collaboration; Probabilistic Principal Surfaces; complex astronomical data set; data classification; data clustering; data mining; data visualization; high dimensional data; massive data sets; multidimensional astronomical data; nonlinear latent variable model; Astronomy; Collaboration; Data analysis; Data mining; Data visualization; Genetics; Humans; Multidimensional systems; Space technology; Topology;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Architecture for Machine Perception, 2005. CAMP 2005. Proceedings. Seventh International Workshop on

Print_ISBN

0-7695-2255-6

Type

conf

DOI

10.1109/CAMP.2005.54

Filename

1508177