As the largest gravitationally bound objects in the Universe, galaxy clusters can be used to probe a variety of topics in astrophysics and cosmology. This thesis describes the development of an algorithm to find galaxy clusters using non-parameteric methods applied to catalogs of galaxies generated from multi-colour CCD observations. It is motivated by the emergence of increasingly large, photometric galaxy surveys and the measurement of key cosmological parameters through the evolution of the cluster mass function. The algorithm presented herein is a reconstruction of the successful, spectroscopic cluster finding algorithm, C4 (Miller et al., 2005), and adapting it to large photometric surveys with the goal of applying it to data from the Dark Energy Survey (DES). AperC4 uses statistical techniques to identify collections of galaxies that are unusually clustered in a multi-dimensional space. To characterize the new algorithm, it is tested with simulations produced by the DES Collaboration and I evaluate its application to photometric datasets. In doing so, I show how AperC4 functions as a cosmology independent cluster finder and formulate metrics for a \successful" cluster finder. Finally, I produce a galaxy catalog appropriate for statistical analysis. C4 is applied to the SDSS galaxy catalog and the resulting cluster catalog is presented with some initial analyses.