  The detection of galaxy clusters in present and future surveys enables
measuring mass-to-light ratios, clustering properties, galaxy cluster
abundances and therefore, constraining cosmological parameters. We present a
new technique for detecting galaxy clusters, which is based on the Matched
Filter Algorithm from a Bayesian point of view. The method is able to determine
the position, redshift and richness of the cluster through the maximization of
a filter depending on galaxy luminosity, density and photometric redshift
combined with a galaxy cluster prior that accounts for color-magnitude
relations and BCG-redshift relation. We tested the algorithm through realistic
mock galaxy catalogs, revealing that the detections are 100% complete and 80%
pure for clusters up to z $<$1.2 and richer than $\Lambda_{CL}>$20 (Abell
Richness $\sim$0, M$\sim4\times10^{14} M_{\odot}$). The completeness and purity
remains approximately the same if we do not include the prior information,
implying that this method is able to detect galaxy cluster with and without a
well defined red sequence. We applied the algorithm to the CFHTLS Archive
Research Survey (CARS) data, recovering similar detections as previously
published using the same or deeper data plus additional clusters which appear
to be real.
