  We describe the application of data mining algorithms to research problems in
astronomy. We posit that data mining has always been fundamental to
astronomical research, since data mining is the basis of evidence-based
discovery, including classification, clustering, and novelty discovery. These
algorithms represent a major set of computational tools for discovery in large
databases, which will be increasingly essential in the era of data-intensive
astronomy. Historical examples of data mining in astronomy are reviewed,
followed by a discussion of one of the largest data-producing projects
anticipated for the coming decade: the Large Synoptic Survey Telescope (LSST).
To facilitate data-driven discoveries in astronomy, we envision a new
data-oriented research paradigm for astronomy and astrophysics --
astroinformatics. Astroinformatics is described as both a research approach and
an educational imperative for modern data-intensive astronomy. An important
application area for large time-domain sky surveys (such as LSST) is the rapid
identification, characterization, and classification of real-time sky events
(including moving objects, photometrically variable objects, and the appearance
of transients). We describe one possible implementation of a classification
broker for such events, which incorporates several astroinformatics techniques:
user annotation, semantic tagging, metadata markup, heterogeneous data
integration, and distributed data mining. Examples of these types of
collaborative classification and discovery approaches within other science
disciplines are presented.
