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K. D. Borne (George Mason University), Z. Huang (Raytheon), J. C. Luna (GMU)
We will present results from an exploratory data mining project to identify classification features of special classes of interacting galaxies (for example, infrared-luminous galaxies) within distributed astronomical databases. Using a variety of data mining techniques, interaction-specific features are learned, to distinguish this class of galaxies from a control sample of normal galaxies. Subsequently, the corresponding rule-based feature model of that class of galaxies is then applied to the large multi-wavelength astronomical databases that are now becoming available. This distributed data mining activity is a prototype science use case for the Virtual Observatory. We specifically apply multi-archive multi-wavelength data to the problem. We will present examples of both successful and unsuccessful data mining attempts.
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The author(s) of this abstract have provided an email address for comments about the abstract: Kirk.Borne@gsfc.nasa.gov
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Bulletin of the American Astronomical Society, 35#5
© 2003. The American Astronomical Soceity.