Application of Decision Trees for Classifying Astronomical Objects

Abstract

Data mining techniques used to analyze and discover data and correlations already present in databases, showed to be very reliable and useful especially when large volumes of data are processed. These techniques have been applied to many areas, such as marketing, medicine, diagnosis, business, biology, astronomy and others. In particular, astronomy requires techniques that allow the recognition or classification of astronomical objects, for example galaxies, stars or quasars, from databases that contain millions of objects. Due to this, astronomers often deal with the analysis of large amounts of data obtained from telescopes, seeking for several characteristics for their interpretation. Decision tree is one of the most used techniques in data mining because of its simplicity to explain the results. Besides, there are decision tree algorithms that work with parallel and incremental techniques, which help to process large databases for classifying new objects faster than traditional algorithms. ParDTLT algorithm, which possesses these characteristics, was used in this work in context of astronomical objects catalogue SDSS, with the aim of obtaining decision rules to help astronomers to understand the behavior patterns of different kinds of astronomical objects.