Recent improvements in telescope construction have resulted in large increases in the volume of data delivered to astronomers, which demand novel methods of evaluation to extract insight. In the spring of 2013, I joined Dr. Kirk Borne in researching the discovery of merging galaxies based on attributes recorded through an automated image processing data pipeline. Using a pre-classified set of 6000 galaxies, we created a program that would accept an arbitrary number of dimensions and run a cluster analysis of the dataset for all combinations of that many spectral attributes. The research was presented as a poster at the George Mason's College of Science Undergraduate Reseach Colloquium, and will be continued in the fall of 2014.