  We present recent results from the LCDM (Laboratory for Cosmological Data
Mining; http://lcdm.astro.uiuc.edu) collaboration between UIUC Astronomy and
NCSA to deploy supercomputing cluster resources and machine learning algorithms
for the mining of terascale astronomical datasets. This is a novel application
in the field of astronomy, because we are using such resources for data mining,
and not just performing simulations. Via a modified implementation of the NCSA
cyberenvironment Data-to-Knowledge, we are able to provide improved
classifications for over 100 million stars and galaxies in the Sloan Digital
Sky Survey, improved distance measures, and a full exploitation of the simple
but powerful k-nearest neighbor algorithm. A driving principle of this work is
that our methods should be extensible from current terascale datasets to
upcoming petascale datasets and beyond. We discuss issues encountered to-date,
and further issues for the transition to petascale. In particular, disk I/O
will become a major limiting factor unless the necessary infrastructure is
implemented.
