Data Mining and Machine Learning in Astronomy

TL;DR

This paper reviews how data mining and machine learning techniques are transforming astronomy by enabling better data analysis, emphasizing proper application to ensure scientific insights rather than black-box results.

Contribution

It provides a comprehensive overview of data mining processes, algorithms, applications, and future directions in astronomy, highlighting best practices for meaningful scientific outcomes.

Findings

Data mining enhances astronomical discoveries when properly applied.

Common algorithms like neural networks and support vector machines are effective.

Future directions include probabilistic methods and petascale computing.

Abstract

We review the current state of data mining and machine learning in astronomy. 'Data Mining' can have a somewhat mixed connotation from the point of view of a researcher in this field. If used correctly, it can be a powerful approach, holding the potential to fully exploit the exponentially increasing amount of available data, promising great scientific advance. However, if misused, it can be little more than the black-box application of complex computing algorithms that may give little physical insight, and provide questionable results. Here, we give an overview of the entire data mining process, from data collection through to the interpretation of results. We cover common machine learning algorithms, such as artificial neural networks and support vector machines, applications from a broad range of astronomy, emphasizing those where data mining techniques directly resulted in improved science, and important current and future directions, including probability density functions, parallel algorithms, petascale computing, and the time domain. We conclude that, so long as one carefully selects an appropriate algorithm, and is guided by the astronomical problem at hand, data mining can be very much the powerful tool, and not the questionable black box.