Task : Scalable Clustering

Problem description: When clustering large data sets it is often not
necessary to use the entire data set to construct a clustering
model. Most clustering algorithms scale either linearly or
quadratically in the size of a data set. Thus, the run-times for
clustering methods can become burdensome when using large data sets.
Meek, Thiesson, and Heckerman (2001) provide a method for selecting
the number of points to use during the construction of a clustering
model and demonstrate the method can lead to substantial speedups
while obtaining clusters with comparable predictive power. Their
analysis is performed on several data sets including the
USCensus1990 data set.

Meek, Thiesson, and Heckerman (2001), "The Learning Curve Method
Applied to Clustering", to appear in The Journal of Machine Learning
Research. (Also see MSR-TR-2001-34 available at
http://research.microsoft.com/scripts/pubs/view.asp?TR_ID=MSR-TR-2001-34)
