A Heuristic evaluation of partitioning techniques considering Early Type Galaxy Databases

Galaxies are one of the most interesting and complex astronomical objects statistically due to their continuous diversification caused mainly due to incidents such as accretion, action, or mergers. Multivariate studies are one of the most useful tools to analyze this type of data and to understand various components of it. We study a sample of the local universe of Orlando 509 galaxies, imputed with Predictive Mean Matching(PMM) multiple imputation algorithm, with the aim of classifying the galaxies into distinct clusters through k-medoids and k-mean algorithms and in turn performing a heuristic evaluation of the two partitioning algorithm through the percentage of misclassification observed. From the clustering algorithms, it was observed that there were four distinct clusters of the galaxies with misclassification of about $1.96\%$. Also comparing the percentage of misclassification heuristically k-means is a superior algorithm to k-medoids under fixed optimal sizes when the said category of galaxy datasets are concerned. By considering that galaxies are continuously evolving complex objects and using appropriate statistical tools, we are able to derive an explanatory classification of galaxies, based on the physical diverse properties of galaxies, and also establish a better method of partitioning when working on the galaxies.