Statistics – Computation
Scientific paper
Dec 2003
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2003aas...203.0409s&link_type=abstract
American Astronomical Society Meeting 203, #04.09; Bulletin of the American Astronomical Society, Vol. 35, p.1208
Statistics
Computation
Scientific paper
The massive amounts of data flooding into the astronomy field hold many answers to important problems in contemporary astrophysics. The biggest problem is sifting through massive amounts of data to uncover these secrets. In this presentation, we identify an approach in which we apply data-mining techniques to the problem of photometric quasar identification. We employ decision trees to quickly and robustly identify potential quasars to a high degree of accuracy. We emphasize computational scalability due to the high volume of data and complexity of the data-mining algorithms.
Auvil L.
Aydt Ruth
Brunner Robert J.
Carpenter Thomas
Redman T.
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