Enhanced cobweb clustering for identifying analog galaxies in astrophysics

Clustering, a very popular task in Data Mining, is unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). Clustering has been explored in many different contexts and disciplines. In this paper, we explore using the COBWEB clustering algorithm to identify and group together galaxies whose spectral energy distribution (SED) is similar. We show that using COBWEB drastically reduces CPU time, compared to a systematic one-by-one comparison previously used in astrophysics. We then extend this approach by using COBWEB clustering with Bootstrap Averaging and show that using Bootstrap Averaging produces a more accurate model in roughly the same amount of time as COBWEB.