  We describe an objective and automated method for detecting clusters of
galaxies from optical imaging data. This method is a variant of the so-called
`matched-filter' technique pioneered by Postman et al. (1996). With
simultaneous use of positions and apparent magnitudes of galaxies, this method
can, not only find cluster candidates, but also estimate their redshifts and
richnesses as byproducts of detection. We examine errors in the estimation of
cluster's position, redshift, and richness with a number of Monte Carlo
simulations. No systematic discrepancies between the true and estimated values
are seen for either redshift or richness. Spurious detection rate of the method
is about less than 10% of those of conventional ones which use only surface
density of galaxies. A cluster survey in the North Galactic Pole is executed to
verify the performance characteristics of the method with real data. Two known
real clusters are successfully detected. We expect these methods based on
`matched-filter' technique to be essential tools for compiling large and
homogeneous optically-selected cluster catalogs.
