  Clusters of galaxies are important laboratories for understanding both galaxy
evolution and constraining cosmological quantities. Any analysis of clusters,
however, is best done when one can reliably determine which galaxies are
members of the cluster. While this would ideally be done spectroscopically, the
difficulty in acquiring a complete sample of spectroscopic redshifts becomes
rather daunting, especially at high redshift where the background contamination
becomes increasingly larger. Traditionally, an alternative approach of applying
a statistical background correction has been utilized, which, while useful in a
global sense, does not provide information for specific galaxies. In this
paper, we develop a more robust technique which uses photometrically estimated
redshifts to determine cluster membership. This technique can either be used as
an improvement over the commonly used statistical correction method or it can
be used to determine cluster candidates on an individual galaxy basis. By
tuning the parameters of our algorithm, we can selectively maximize our
completeness or, alternatively, minimize our contamination. Furthermore, our
technique provides a statistical quantification of both our resulting
completeness and contamination from foreground and background galaxies.
