Data Mining and Machine Learning in Astronomy by Nicholas M. Ball and Robert J. Brunner. (International Journal of Modern Physics D, Volume 19, Issue 07, pp. 1049-1106 (2010).)
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 in which data mining techniques directly contributed to improving science, and important current and future directions, including probability density functions, parallel algorithms, Peta-Scale 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.
At fifty-eight (58) pages and three hundred and seventy-five references, this is a great starting place to learn about data mining and machine learning from an astronomy perspective!
And should yield new techniques or new ways to apply old ones to your data, with a little imagination.
Dates from 2010 so word of more recent surveys welcome!
[…] Data Mining and Machine Learning in Astronomy #topicmaps #datamining #astroinformatics #ml – http://t.co/KKMJN9li… […]
Pingback by Data Mining and Machine Learning in Astronomy "Another Word For It" | Business Intelligence Needs ? | Scoop.it — November 24, 2012 @ 5:21 am