  We describe an automated method, the Cut & Enhance method (CE) for detecting
clusters of galaxies in multi-color optical imaging surveys. This method uses
simple color cuts, combined with a density enhancement algorithm, to up-weight
pairs of galaxies that are close in both angular separation and color. The
method is semi-parametric since it uses minimal assumptions about cluster
properties in order to minimize possible biases. No assumptions are made about
the shape of clusters, their radial profile or their luminosity function. The
method is successful in finding systems ranging from poor to rich clusters of
galaxies, of both regular and irregular shape. We determine the selection
function of the CE method via extensive Monte Carlo simulations which use both
the real, observed background of galaxies and a randomized background of
galaxies. We use position shuffled and color shuffled data to perform the false
positive test. We have also visually checked all the clusters detected by the
CE method. We apply the CE method to the 350 deg^2 of the SDSS (Sloan Digital
Sky Survey) commissioning data and construct a SDSS CE galaxy cluster catalog
with an estimated redshift and richness for each cluster. The CE method is
compared with other cluster selection methods used on SDSS data such as the
Matched Filter (Postman et al. 1996, Kim et al. 2001), maxBCG technique (Annis
et al. 2001) and Voronoi Tessellation (Kim et al. 2001). The CE method can be
adopted for cluster selection in any multi-color imaging surveys.
