  One important tool is the optimal clustering of data into useful categories.
Dividing similar objects into a smaller number of clusters is of importance in
many applications. These include search engines, monitoring of academic
performance, biology and wireless networks. We first discuss a number of
clustering methods. We present a parallel algorithm for the efficient
clustering of objects into groups based on their similarity to each other. The
input consists of an n by n distance matrix. This matrix would have a distance
ranking for each pair of objects. The smaller the number, the more similar the
two objects are to each other. We utilize parallel processors to calculate a
hierarchal cluster of these n items based on this matrix. Another advantage of
our method is distribution of the large n by n matrix. We have implemented our
algorithm and have found it to be scalable both in terms of processing speed
and storage.
