Abstract
In the past few years, we have observed a trend of increasing cooperation between computer science and other empirical sciences such as physics, biology, or medical fields. This e-science synergy opens new challenges for the computer science and triggers important advances in other areas of research. In our particular case, we are facing an astroinformatics challenge of analysing stellar spectra in order to establish automated classification methods for recognizing different types of Be stars. We have chosen similarity search methods, which are effectively utilized in other domains like multimedia content-based retrieval for instance. This paper presents our analysis of the problematics and proposed a solution based on Signature Quadratic Form Distance and feature signatures. We have also conducted intensive empirical evaluation which allowed us to determine appropriate configuration for our similarity model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ball, N.M., Brunner, R.J.: Data mining and machine learning in astronomy. International Journal of Modern Physics D 19(07), 1049–1106 (2010), http://www.worldscientific.com/doi/abs/10.1142/S0218271810017160
Bazarghan, M.: Application of self-organizing map to stellar spectral classifications. Astrophysics and Space Science 337(1), 93–98 (2012)
Bazell, D., Miller, D.J., SubbaRao, M.: Objective subclass determination of Sloan Digital Sky Survey spectroscopically unclassified objects. The Astrophysical Journal 649(2), 678 (2006), http://stacks.iop.org/0004-637X/649/i=2/a=678
Beecks, C., Uysal, M., Seidl, T.: Signature quadratic form distances for content-based similarity. In: 17th ACM International Conference on Multimedia, pp. 697–700. ACM (2009)
Beecks, C., Uysal, M., Seidl, T.: Signature quadratic form distance. In: Proceedings of the ACM International Conference on Image and Video Retrieval, pp. 438–445. ACM (2010)
Beecks, C., Lokoč, J., Seidl, T., Skopal, T.: Indexing the signature quadratic form distance for efficient content-based multimedia retrieval. In: Proceedings of the 1st ACM International Conference on Multimedia Retrieval, p. 24. ACM (2011)
Bin, J., Chang, P.J., Ping, Y.Z., Qiang, G.: A data mining application in stellar spectra. In: International Symposium on Computer Science and Computational Technology, ISCSCT 2008, vol. 2, pp. 66–69 (2008)
Borne, K.D.: Scientific data mining in astronomy. In: Next Generation of Data Mining, pp. 91–114 (2009)
Bracewell, R.N., Bracewell, R.: The Fourier transform and its applications, vol. 31999. McGraw-Hill, New York (1986)
Bromová, P., Škoda, P., Zendulka, J.: Wavelet Based Feature Extraction for Clustering of Be Stars. In: Zelinka, I., Chen, G., Rössler, O.E., Snasel, V., Abraham, A. (eds.) Nostradamus 2013: Prediction, Model. & Analysis. AISC, vol. 210, pp. 467–474. Springer, Heidelberg (2013)
Krulis, M., Skopal, T., Lokoc, J., Beecks, C.: Combining CPU and GPU architectures for fast similarity search. Distributed and Parallel Databases 30(3-4), 179–207 (2012)
Lokoč, J., Hetland, M., Skopal, T., Beecks, C.: Ptolemaic indexing of the signature quadratic form distance. In: Proceedings of the Fourth International Conference on SImilarity Search and Applications, pp. 9–16. ACM (2011)
Yamaguchi, F., Yamaguchi, F.: Curves and surfaces in computer aided geometric design. Springer, Berlin (1988)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Kruliš, M., Bednárek, D., Yaghob, J., Zavoral, F. (2014). Employing Similarity Methods for Stellar Spectra Classification in Astroinformatics. In: Traina, A.J.M., Traina, C., Cordeiro, R.L.F. (eds) Similarity Search and Applications. SISAP 2014. Lecture Notes in Computer Science, vol 8821. Springer, Cham. https://doi.org/10.1007/978-3-319-11988-5_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-11988-5_21
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11987-8
Online ISBN: 978-3-319-11988-5
eBook Packages: Computer ScienceComputer Science (R0)