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---+ [[IvoaKDDguide][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

Books

   * Way M., et al. (eds.), "Advances in Machine Learning and Data Mining for Astronomy", CRC Press, forthcoming Oct 26th 2011; http://www.giss.nasa.gov/staff/mway/book

Review Papers

   * 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
   * Borne K., "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
   * 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)
   * Hassan, A. & Fluke, C.J., "Scientific Visualization in Astronomy: Towards the Petascale Astronomy Era", PASA accepted, arXiv/1102.5123 (2011)


   * Tagliaferri R., et al., "Neural networks in astronomy", Neural Networks 16 (2003) 297
   * 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

   * Borne K.D. et al., "Astroinformatics: A 21st Century Approach to Astronomy", white paper submitted to the US Astro 2010 decadal review of astronomy; http://www8.nationalacademies.org/astro2010/DetailFileDisplay.aspx?id=455
   * Ferguson H.C., et al., "Astronomical Data Reduction and Analysis for the Next Decade", Astro2010; http://www8.nationalacademies.org/astro2010/DetailFileDisplay.aspx?id=426
   * Loredo T.J., et al., "The Astronomical Information Sciences: A Keystone for 21st-Century Astronomy", Astro2010; http://www8.nationalacademies.org/astro2010/DetailFileDisplay.aspx?id=439

   * Ball N.M. & Schade D., "Astroinformatics in Canada", white paper submitted to the Canadian Long Range Plan in Astronomy (LRP 2010); http://www.casca.ca/lrp2010/Docs/LRPReports/astroinformatics_lrp.pdf
   * Dursi L.J., "Canadian Astronomical Computing, Data, and Network Facilities", LRP2010; http://www.casca.ca/lrp2010/Docs/LRPReports/CDandN_WP.pdf

Websites

   * http://www.practicalastroinformatics.org --- Astroinformatics
   * http://ivoa.net/newsletter --- Newsletter of the International Virtual Observatory, including links to useful software, and VO-enabled science

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

   * http://astrostatistics.psu.edu  --- Penn State Center for Astrostatistics
   * http://www.stat.cmu.edu/~inca --- International Computational Astrostatistics Group
   * http://hea-www.harvard.edu/AstroStat --- California/Boston/Smithsonian Astrostatistics Collaboration

   * http://astrodatamining.net --- Data Mining in Astronomy blog, with links
   * http://groundtruth.info/AstroStat/slog --- Astrostatistics blog


---+++ General Data Mining Links

Books

   * 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, 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, 2005).
   * Bishop C.M., Pattern Recognition and Machine Learning (Springer, New York, 2007).

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

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

Papers

   * 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"

Websites

   * http://www.kdnuggets.com --- General data mining website with numerous resources
   * http://www.kdd.org --- Knowledge discovery in databases, a more broadly-based site on this group's interest
   * http://www.autonlab.org/tutorials --- Statistical Data Mining Tutorials
   * http://en.wikipedia.org/wiki/Category:Machine_learning --- Wikipedia has many useful introductory articles for the non-specialist

Software

   * http://www.cs.waikato.ac.nz/ml/weka --- WEKA, a commonly used data mining environment
   * http://rapid-i.com --- !RapidMiner, along with WEKA, the most commonly used data mining software
   * http://www.r-project.org --- R, widely used in statistics, including astrostatistics
   * http://mloss.org/software --- Open-source machine learning software


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-- IVOA.NickBall - 04 Apr 2011

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Topic revision: r6 - 2011-04-04 - NickBall
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