A Comparative Study of KBS, ANN and Statistical Clustering Techniques for Unattended Stellar Classification

  • Carlos Dafonte
  • Alejandra Rodríguez
  • Bernardino Arcay
  • Iciar Carricajo
  • Minia Manteiga
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)


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.


Expert System Spectral Parameter Input Pattern Spectral Type Human Expert 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Zombeck, M.V.: Handbook of Astronomy and Astrophysics, 2nd edn. Cambridge University Press, Cambridge (1990)Google Scholar
  2. 2.
    Morgan, W.W., Keenan, P.C., Kellman, E.: An Atlas of Stellar Spectra with an outline of Spectral Classification, University of Chicago Press, Chicago (1943)Google Scholar
  3. 3.
    Weaber, W., Torres-Dodgen, A.: Neural Networks Classification of the near-infrared spectra of A-type Stars. The Astrophysical Journal 446, 300–317 (1995)CrossRefGoogle Scholar
  4. 4.
    Rodriguez, A., Arcay, B., Dafonte, C., Manteiga, M., Carricajo, I.: Automated knowledgebased analysis and classification of stellar spectra using fuzzy reasoning. Expert Systems with Applications 27(2), 237–244 (2004)CrossRefGoogle Scholar
  5. 5.
    Silva, D.R., Cornell, M.E.: A New Library of Stellar Optical Spectra. The Astrophysical Journal Suppl. 81(2), 865–881 (1992)CrossRefGoogle Scholar
  6. 6.
    Pickles, A.J.: A Stellar Spectral Flux Library. 1150-25000 Å, Publications of the Astronomical Society of the Pacific 110, 863–878 (1998)CrossRefGoogle Scholar
  7. 7.
    Jacoby, G.H., Hunter, D.A., Christian, C.A.: A Library of Stellar Spectra. The Astrophysical Journal Suppl. 56, 257–281 (1994)CrossRefGoogle Scholar
  8. 8.
    Sowa, J.F.: Knowledge Representation: Logical and Computational. Brooks Cole Publishing Co. (1999)Google Scholar
  9. 9.
    Valette-Florence, P.: Introduction to Means-End Chain Analysis. Rech. Appl. Mark 9, 93–117 (1994)Google Scholar
  10. 10.
    Buchanan, B., Shortliffe, E.: Ruled-based Expert Systems. Addison-Wesley, Reading (1984)Google Scholar
  11. 11.
    Mendel, J.M.: Fuzzy Logic Systems for Engineering: A Tutorial. Proceedings of the IEEE 83(3), 345–377 (1995)CrossRefGoogle Scholar
  12. 12.
    Forgy, C.L.: The OPS/R2 User’s Manual. Production Systems Technologies Inc. (1995)Google Scholar
  13. 13.
    Haykin, S.: Neural Networks. A Comprehensive Foundation. MacMillan Coll. Pub., Basingstoke (1994)zbMATHGoogle Scholar
  14. 14.
    Kohonen, T.: Self-Organization and Associative Memory. Springer, Heidelberg (1987)Google Scholar
  15. 15.
    Everitt, B., et al.: Cluster analysis. Edward Arnold Publishers Ltd. (2001)Google Scholar
  16. 16.
    Kaufman, L., Rousseuw, P.J.: Finding groups in Data. Wiley, Chichester (1990)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Carlos Dafonte
    • 1
  • Alejandra Rodríguez
    • 1
  • Bernardino Arcay
    • 1
  • Iciar Carricajo
    • 2
  • Minia Manteiga
    • 2
  1. 1.Information and Communications Technologies Department, Faculty of Computer ScienceUniversity of A CoruñaA CoruñaSpain
  2. 2.Navigation and Earth Sciences DeptartmentUniversity of A CoruñaA CoruñaSpain

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