General

Review Papers

1. N. M. Ball & R.J. Brunner, Data Mining and Machine Learning in Astronomy, International Journal of Modern Physics D 19 (2010) 1049; arXiv/0906.2173
2. K. Borne, Scientific Data Mining in Astronomy, Data Mining and Knowledge Discovery Series Data Mining and Knowledge Discovery Series, (Taylor & Francis: CRC Press, Boca Raton, FL, 2009), Ch. 5, pp. 91–114.
3. R. Tagliaferri et al., Neural networks in astronomy, Neural Networks 16 (2003) 297.

Books

1. T. Hey, S. Tansley and K. Talle (eds.), The Fourth Paradigm: Data-Intensive Scientific Discovery (Microsoft Research, Redmond, WA, 2009).
2. C. Kamath, Scientific Data Mining: A Practical Perspective (Society for Industrial and Applied Mathematics, Philadelphia, PA, 2009).
3. T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer Series in Statistics, 2nd edn. (Springer,

1. I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann Series in Data Management Systems, 2nd edn. (Morgan Kaufmann, San Francisco, 2005).
2. C. M. Bishop, Pattern Recognition and Machine Learning (Springer, New York, 2007).

Websites

1. http://www.kdnuggets.com
2. http://www.kdd.org
3. http://www.autonlab.org/tutorials
4. http://en.wikipedia.org/wiki/Category:Machine_learning
5. http://www.practicalastroinformatics.org/

Specific Methods

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

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Software

1. http://www.cs.waikato.ac.nz/ml/weka/
2. http://pcdevauc.na.infn.it:9000/