Clustering Analysis Algorithms and Their Applications to Digital POSS-II Catalogs
We report on the preliminary results of experiments using a Bayesian cluster method to cluster objects present in photographic images of the POSS-II. Our goal is to explore the power of unsupervised learning techniques to classify objects meaningfully, and perhaps to discover previously unrecognized object categories in digital sky surveys. Our primary nding is that the program we used, AutoClass, was able to form several sensible categories from a few simple attributes of the object images, separating the data into four recognizable and astronomically meaningful classes: stars, galaxies with bright central cores, galaxies without bright cores, and stars with a visible \fuzz" around them. Also, in an independent experiment we found out that the two types of galaxies have distinct color distributions (the more concentrated class being redder, as indeed expected if they are predominantly early Hubble types), although no color information was given to AutoClass. This illustrates the power of unsupervised classiication techniques to discriminate between astronomically distinct types of objects on the basis of data alone. We believe that the application of such algorithms to large-scale astronomical sky surveys can aid in cataloging the detected objects, and may even have the potential to discover new categories of objects.