  We present recent results from the Laboratory for Cosmological Data Mining
(http://lcdm.astro.uiuc.edu) at the National Center for Supercomputing
Applications (NCSA) to provide robust classifications and photometric redshifts
for objects in the terascale-class Sloan Digital Sky Survey (SDSS). Through a
combination of machine learning in the form of decision trees, k-nearest
neighbor, and genetic algorithms, the use of supercomputing resources at NCSA,
and the cyberenvironment Data-to-Knowledge, we are able to provide improved
classifications for over 100 million objects in the SDSS, improved photometric
redshifts, and a full exploitation of the powerful k-nearest neighbor
algorithm. This work is the first to apply the full power of these algorithms
to contemporary terascale astronomical datasets, and the improvement over
existing results is demonstrable. We discuss issues that we have encountered in
dealing with data on the terascale, and possible solutions that can be
implemented to deal with upcoming petascale datasets.
