IVOA KDD-IG: A user guide for Data Mining in Astronomy

8: Links: Books, papers, websites

There are a large number of books, papers, and websites about data mining. This section does not aim to exhaustively list everything, but to point to those resources that are most useful for astronomy. We start with links specific to astronomy, followed by more general data mining links that are useful to astronomers.

Links specific to Astronomy


  • Way M., Scargle J.D., Ali K.M., Srivastava A.N. (eds.), Advances in Machine Learning and Data Mining for Astronomy (Data Mining and Knowledge Discovery Series, CRC Press, Boca Raton, FL, 2012)

Review Papers

  • Graham, M., The Art of Data Science, arXiv/1106.3305; invited talk at Astrostatistics and Data Mining in Large Astronomical Databases workshop, La Palma, Spain, 30 May - 3 June 2011, to appear in Springer Series on Astrostatistics
  • Ball N.M. & Brunner R.J., Data Mining and Machine Learning in Astronomy, International Journal of Modern Physics D 19 (7) 1049-1106 (2010); arXiv/0906.2173; http://ned.ipac.caltech.edu/level5/March11/Ball/frames.html
  • Borne K.D., Astroinformatics: data-oriented astronomy research and education, Earth Science Informatics 3(1-2): 5-17 (2010), http://dx.doi.org/10.1007/s12145-010-0055-2
  • Feigelson E.D., Cross-disciplinary research in astronomy, Proceedings of Science (2010)
  • Pesenson M.Z, Pesenson I.Z. & McCollum B., The Data Big Bang and the Expanding Digital Universe: High-Dimensional, Complex and Massive Data Sets in an Inflationary Epoch (2010)
  • Borne K.D., Scientific Data Mining in Astronomy, Data Mining and Knowledge Discovery Series, Taylor & Francis: CRC Press, Boca Raton, FL, Ch. 5, pp. 91–114 (2009); arXiv/0911.0505

  • Hassan A. & Fluke C.J., PASA 28 150 (2011): Scientific Visualization in Astronomy: Towards the Petascale Astronomy Era

  • Tagliaferri R., et al., Neural networks in astronomy, Neural Networks 16 297 (2003)
  • Vistas in Astronomy 38 (1994), Special Issue on Artificial Neural Networks in Astronomy
  • Miller A.S., A Review of Neural Network Applications in Astronomy, Vistas in Astronomy 36 141-161 (1993)

  • Charbonneau P., Genetic Algorithms in Astronomy and Astrophysics, ApJS 101 309 (1995)

White Papers


  • https://asaip.psu.edu --- The Astrostatistics and Astroinformatics Portal is a multi-community outreach vehicle for four organizations: the International Statistical Institute (ISI), International Astronomical Union (IAU), American Astronomical Society (AAS), and the Large Synoptic Survey Telescope (LSST)
  • http://ivoa.net/newsletter --- Newsletter of the International Virtual Observatory, including links to useful software, and VO-enabled science
  • http://www.practicalastroinformatics.org --- Astroinformatics

  • http://dame.dsf.unina.it --- DAME, Data Mining and Exploration, a web-based distributed data mining infrastructure
  • http://canfar.phys.uvic.ca --- CANFAR, a cloud computing environment for astronomers, designed to provide the infrastructure on which to build a data processing project



General Data Mining Links


  • Hey T., Tansley S. & Talle K. (eds.), The Fourth Paradigm: Data-Intensive Scientific Discovery (Microsoft Research, Redmond, WA, 2009)
  • Kamath C., Scientific Data Mining: A Practical Perspective (Society for Industrial and Applied Mathematics, Philadelphia, PA, 2009)
  • Hastie T., Tibshirani R. & Friedman J., The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer Series in Statistics, 2nd edn. (Springer, New York, NY, 2009)
  • Witten I.H. & Frank E., Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann Series in Data Management Systems, 2nd edn. (Morgan Kaufmann, San Francisco, CA, 2005)
  • Bishop C.M., Pattern Recognition and Machine Learning (Springer, New York, NY, 2007)

  • Gregory P., Bayesian Logical Data Analysis for the Physical Sciences (Cambridge University Press, Cambridge, UK, 2005)

  • Tufte, E.R., The Visual Display of Quantitative Information, 2nd edn. (Graphics Press, Cheshire, CT, 2001)

  • Bishop C.M., Neural Networks for Pattern Recognition (Oxford University Press, Oxford, UK, 1995)
  • Ripley B.D., Pattern Recognition and Neural Networks (Cambridge University Press, Cambridge, UK, 2008)


  • Hand D.J., Statistical Science 21 1 (2006): Classifier Technology and the Illusion of Progress
  • Kamath C., Journal of Physics Conference Series 125 012094 (2008): Sapphire: Experiences in Scientific Data Mining



-- NickBall - 19 Mar 2011
-- NickBall - 23 Sep 2011
-- NickBall - 05 Oct 2011
-- NickBall - 07 May 2012

Topic revision: r14 - 2013-01-16 - NickBall
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