  Clusters of galaxies are the most massive objects in the Universe and mapping
their location is an important astronomical problem. This paper describes an
algorithm (based on statistical signal processing methods), a software
architecture (based on a hybrid layered approach) and a parallelization scheme
(based on a client/server model) for finding clusters of galaxies in large
astronomical databases. The Adaptive Matched Filter (AMF) algorithm presented
here identifies clusters by finding the peaks in a cluster likelihood map
generated by convolving a galaxy survey with a filter based on a cluster model
and a background model. The method has proved successful in identifying
clusters in real and simulated data. The implementation is flexible and readily
executed in parallel on a network of workstations.
