Data Mining and Machine Learning in Astronomy

By N. Ball, Robert J. Brunner Herzberg Institute of Astrophysics, Victoria, bc, Canada., D. O. Astronomy, U. I. Urbana-Champaign. 2009

308 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Instrumentation, Signal Processing

View Paper: https://www.semanticscholar.org/paper/6241276a3074f73c1ce9b9ce4ac69f748732aecd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Ann B. Lee
2. Rafael Izbicki
3. Ofer Lahav
4. Wouter M. Kouw
5. Taylor Pospisil

In Instrumentation

1. Ofer Lahav

In Signal Processing

1. Ofer Lahav
2. Ann B. Lee
3. Rafael Izbicki
4. Wouter M. Kouw

Citations in the corpus

• Nonparametric Conditional Density Estimation in a High-Dimensional Regression Setting (2016, background): “Indeed, in a review of the current state of data mining and machine learning in astronomy, Ball and Brunner (2010) listed working with probability densities as one of the “future trends” of the field.”
• PHOTOMETRIC SUPERNOVA CLASSIFICATION WITH MACHINE LEARNING (2016, methodology): “As astronomical datasets become larger and more difficult to process, machine learning has become increasingly popular (Ball & Brunner 2010; Bloom & Richards 2012).”
• An introduction to domain adaptation and transfer learning (2018, background): “Learning algorithms are now incorporated into self-driving cars [201], drone guidance [81], computer-assisted diagnosis [122], online commerce [142], satellite cartography [207], exo-planet discovery [9], and machine translation [216], among others.”