Advances in Machine Learning and Data Mining for Astronomy

Michael J. Way, Jeffrey D. Scargle, Kamal M. Ali, Ashok N. Srivastava

March 29, 2012 by Chapman and Hall/CRC
Reference - 744 Pages - 33 Color & 177 B/W Illustrations
ISBN 9781439841730 - CAT# K11942
Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery Series


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  • Provides the first comprehensive book on data mining and machine learning techniques and tools for astronomy applications
  • Describes the historical relationship between statistics and astronomy
  • Covers the identification and preliminary characterization of sources of astronomical surveys in several contexts, ranging from long (microwave) to short (gamma rays) wavelengths
  • Explores gravitational lensing, identifying organic molecules in space, and estimating galaxy redshifts
  • Shows how time-series analyses have expanded into new astronomical domains
  • Examines how astronomers and computational scientists are dealing with large quantities of complex data now and in the future
  • Discusses how various machine learning methods, such as time–frequency distributions, classification, cluster analysis, and pattern recognition, are used in astronomy


Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science.

The book’s introductory part provides context to issues in the astronomical sciences that are also important to health, social, and physical sciences, particularly probabilistic and statistical aspects of classification and cluster analysis. The next part describes a number of astrophysics case studies that leverage a range of machine learning and data mining technologies. In the last part, developers of algorithms and practitioners of machine learning and data mining show how these tools and techniques are used in astronomical applications.

With contributions from leading astronomers and computer scientists, this book is a practical guide to many of the most important developments in machine learning, data mining, and statistics. It explores how these advances can solve current and future problems in astronomy and looks at how they could lead to the creation of entirely new algorithms within the data mining community.

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