Introduction

Abstract

The fourth paradigm [19], to which it is now referred, describes the emergence of data mining within the various scientific disciplines, including that of astronomy. The Sloan Digital Sky Survey (SDSS) alone possesses, at present, over 1,000,000 galaxies, 30,000 stars and 100,000 quasars collated into several data sets [141].With such copious amounts of data constantly being acquired from various astronomical surveys, it now becomes imperative that an automated model to processing this data be developed so as to be able to generate useful information. The goal of this approach is to then produce an outcome that will result in effective human learning. It is the process of characterising the known, assigning the new and discovering the unknown in such a data-intensive discipline that encompasses what astronomical data mining is all about [28]. Big Data is the recently coined term, describing technologies that deal with large volumes of data arriving at high speed. This is the typical description of what our state-of-the-art telescopes are capturing every day from stars and galaxies to back holes and dark matter.