Interrelation analysis of celestial spectra data using constrained frequent pattern trees

Association rule mining, in which generating frequent patterns is a key step, is an effective way of identifying inherent and unknown interrelationships between characteristics of celestial spectra data and its physicochemical properties. In this study, we first make use of the first-order predicate logic to represent knowledge derived from celestial spectra data. Next, we propose a concept of constrained frequent pattern trees (CFP) along with an algorithm used to construct CFPs, aiming to improve the efficiency and pertinence of association rule mining. Finally, we quantitatively evaluate the CPU and I/O performance of our novel interrelation analysis method using a variety of real-world data sets. Our experimental results show that it is practical to study the laws of celestial bodies using our new interrelation analysis method to discover correlations between celestial spectra data characteristics and the physicochemical properties.