Links specific to Astronomy

• 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

• 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)

• 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)

• 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

• 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

• 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).

• 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