Machine learning is a broad discipline that comprises a variety of techniques for extracting meaningful information and patterns from data. It draws on knowledge and "know-how" from various scientific areas such as statistics, graph theory, linear algebra, databases, mathematics, and computer science. Recently, materials scientists have begun to explore data mining ideas for discovery in materials. In this paper we explore the power of these methods for studying binary compounds that are well characterized and are often used as a test bed. By mining properties of the constituent atoms, three materials research relevant tasks, namely, separation of a number of compounds into subsets in terms of their crystal structure, grouping of an unknown compound into the most characteristically similar peers (in one instance, 100% accuracy is achieved), and specific property prediction (the melting point), are explored.