Data mining techniques on astronomical spectra data – III. Association analysis

Abstract

ABSTRACT Association analysis is an important task that aims to investigate correlations in astronomical spectral data and mine relationships between different data features. With the rapid development of various sky survey projects, multiple association analysis methods have been applied to efficiently investigate the correlation between spectral data. However, due to the different focuses of analysing the relationships within spectral data, the performance and applicability scenarios of association analysis methods vary. We present the third article in the series to provide a comprehensive review of algorithms for astronomical spectral association analysis. First, this paper outlines the ideas and applications of association analysis algorithms for astronomical spectra in the current literature. Secondly, experiments are conducted on a unified A-type stellar spectral data set constructed based on three different signal-to-noise ratios and data volumes to examine the performance of different algorithms in analysing the correlation between data features. The results indicate that association rule algorithms can more comprehensively and effectively uncover the correlations among different spectral features, while regression analysis algorithms offer a simpler and more intuitive approach to analysing relationships between features. The spectral data used in the experiments are obtained from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope. Finally, the source code of association analysis algorithms and manuals for usage are provided on GitHub.