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Abstract: We describe the application of data mining algorithms to research problems inastronomy. We posit that data mining has always been fundamental toastronomical research, since data mining is the basis of evidence-baseddiscovery, including classification, clustering, and novelty discovery. Thesealgorithms represent a major set of computational tools for discovery in largedatabases, which will be increasingly essential in the era of data-intensiveastronomy. Historical examples of data mining in astronomy are reviewed,followed by a discussion of one of the largest data-producing projectsanticipated for the coming decade: the Large Synoptic Survey Telescope LSST.To facilitate data-driven discoveries in astronomy, we envision a newdata-oriented research paradigm for astronomy and astrophysics -astroinformatics. Astroinformatics is described as both a research approach andan educational imperative for modern data-intensive astronomy. An importantapplication area for large time-domain sky surveys such as LSST is the rapididentification, characterization, and classification of real-time sky eventsincluding moving objects, photometrically variable objects, and the appearanceof transients. We describe one possible implementation of a classificationbroker for such events, which incorporates several astroinformatics techniques:user annotation, semantic tagging, metadata markup, heterogeneous dataintegration, and distributed data mining. Examples of these types ofcollaborative classification and discovery approaches within other sciencedisciplines are presented.

Autor: Kirk Borne 1 1 George Mason University


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