A comparative study of KBS, ANN and statistical clustering techniques for unattended stellar classification

Authors:
Carlos Dafonte;Alejandra Rodríguez;Bernardino Arcay;Iciar Carricajo;Minia Manteiga
Affiliations:
Information and Communications Technologies Department, Faculty of Computer Science, University of A Coruña, A Coruña, Spain;Information and Communications Technologies Department, Faculty of Computer Science, University of A Coruña, A Coruña, Spain;Information and Communications Technologies Department, Faculty of Computer Science, University of A Coruña, A Coruña, Spain;Navigation and Earth Sciences Deptartment, University of A Coruña, A Coruña, Spain;Navigation and Earth Sciences Deptartment, University of A Coruña, A Coruña, Spain
Venue:
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
Year:
2005

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Abstract

The purpose of this work is to present a comparative analysis of knowledge-based systems, artificial neural networks and statistical clustering algorithms applied to the classification of low resolution stellar spectra. These techniques were used to classify a sample of approximately 258 optical spectra from public catalogues using the standard MK system. At present, we already dispose of a hybrid system that carries out this task, applying the most appropriate classification method to each spectrum with a success rate that is similar to that of human experts.