Difference: IvoaKDDguideLinks (1 vs. 14)

Revision 142013-01-16 - NickBall

 
META TOPICPARENT name="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

Changed:
<
<
  • 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)
>
>
  • 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

Changed:
<
<
  • 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. 91114 (2009); arXiv/0911.0505
>
>
  • 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
 
Changed:
<
<
  • Hassan A. & Fluke C.J., PASA 28 150 (2011): Scientific Visualization in Astronomy: Towards the Petascale Astronomy Era
>
>
  • Hassan A. & Fluke C.J., PASA 28 150 (2011): Scientific Visualization in Astronomy: Towards the Petascale Astronomy Era
 
Changed:
<
<
  • 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)
>
>
  • 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)
 
Changed:
<
<
  • Charbonneau P., Genetic Algorithms in Astronomy and Astrophysics, ApJS 101 309 (1995)
>
>
  • Charbonneau P., Genetic Algorithms in Astronomy and Astrophysics, ApJS 101 309 (1995)
 

White Papers

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Websites

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  • 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
 
Changed:
<
<
  • 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
>
>
  • 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
 
Changed:
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Changed:
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<
>
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Blogs

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>
>
 

Podcast

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<
>
>
 

General Data Mining Links

Books

Changed:
<
<
  • 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)
>
>
  • 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)
 
Changed:
<
<
  • Gregory P., Bayesian Logical Data Analysis for the Physical Sciences (Cambridge University Press, Cambridge, UK, 2005)
>
>
  • Gregory P., Bayesian Logical Data Analysis for the Physical Sciences (Cambridge University Press, Cambridge, UK, 2005)
 
Changed:
<
<
  • Tufte, E.R., The Visual Display of Quantitative Information, 2nd edn. (Graphics Press, Cheshire, CT, 2001)
>
>
  • Tufte, E.R., The Visual Display of Quantitative Information, 2nd edn. (Graphics Press, Cheshire, CT, 2001)
 
Changed:
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<
  • 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)
>
>
  • 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)
 

Papers

Changed:
<
<
  • 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
>
>
  • 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

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<
<
>
>
 

Software

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Added:
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-- NickBall - 19 Mar 2011
-- NickBall - 23 Sep 2011
-- NickBall - 05 Oct 2011
-- NickBall - 07 May 2012
 
Deleted:
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<

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


 
Deleted:
<
<

Revision 132012-06-26 - root

 
META TOPICPARENT name="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., 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. 91114 (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

Websites

  • 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

Blogs

Podcast

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, 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)

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

Software


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


Revision 122012-05-07 - NickBall

 
META TOPICPARENT name="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.

Deleted:
<
<
 

Links specific to Astronomy

Books

Changed:
<
<
>
>
  • 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. 91114 (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

Websites

Changed:
<
<
>
>
 
  • http://ivoa.net/newsletter --- Newsletter of the International Virtual Observatory, including links to useful software, and VO-enabled science
Added:
>
>
 
  • 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

Added:
>
>
 

Blogs

Added:
>
>

Podcast

 
Added:
>
>
 

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, 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)

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

Software


-- NickBall - 19 Mar 2011
-- NickBall - 23 Sep 2011
-- NickBall - 05 Oct 2011

Added:
>
>

-- NickBall - 07 May 2012
 

<--  
-->

Revision 112011-10-05 - NickBall

 
META TOPICPARENT name="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

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
Changed:
<
<
  • Borne K., Scientific Data Mining in Astronomy, Data Mining and Knowledge Discovery Series, Taylor & Francis: CRC Press, Boca Raton, FL, Ch. 5, pp. 91114 (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)
Added:
>
>
  • Borne K.D., Scientific Data Mining in Astronomy, Data Mining and Knowledge Discovery Series, Taylor & Francis: CRC Press, Boca Raton, FL, Ch. 5, pp. 91114 (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

Websites

  • 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

Blogs

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, 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)

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

Software


-- NickBall - 19 Mar 2011
-- NickBall - 23 Sep 2011

Changed:
<
<
-- NickBall - 03 Oct 2011
>
>
-- NickBall - 05 Oct 2011
 

<--  
-->

Revision 102011-10-03 - NickBall

 
META TOPICPARENT name="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

Changed:
<
<
Review Papers
>
>

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., Scientific Data Mining in Astronomy, Data Mining and Knowledge Discovery Series, Taylor & Francis: CRC Press, Boca Raton, FL, Ch. 5, pp. 91114 (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., PASA 28 150 (2011): Scientific Visualization in Astronomy: Towards the Petascale Astronomy Era
Changed:
<
<
  • Tagliaferri R., et al., Neural networks in astronomy, Neural Networks 16 (2003) 297
>
>
  • 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

Websites

Changed:
<
<
  • 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://canfar.phys.uvic.ca --- CANFAR, a cloud computing environment for astronomers, designed to provide the infrastructure on which to build a data processing project
 

Blogs

Changed:
<
<
>
>
 

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, 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)

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

Software


-- NickBall - 19 Mar 2011
-- NickBall - 23 Sep 2011
-- NickBall - 03 Oct 2011


<--  
-->

Revision 92011-10-03 - NickBall

 
META TOPICPARENT name="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

Changed:
<
<
Books
>
>

Books

 
Changed:
<
<
>
>
  Review Papers
Changed:
<
<
  • 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., "Scientific Data Mining in Astronomy", Data Mining and Knowledge Discovery Series, Taylor & Francis: CRC Press, Boca Raton, FL, Ch. 5, pp. 91114 (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)
>
>
  • 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., Scientific Data Mining in Astronomy, Data Mining and Knowledge Discovery Series, Taylor & Francis: CRC Press, Boca Raton, FL, Ch. 5, pp. 91114 (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)
 
Changed:
<
<
  • Hassan A. & Fluke C.J., PASA 28 150 (2011): "Scientific Visualization in Astronomy: Towards the Petascale Astronomy Era"
>
>
  • Hassan A. & Fluke C.J., PASA 28 150 (2011): Scientific Visualization in Astronomy: Towards the Petascale Astronomy Era
 
Changed:
<
<
  • Tagliaferri R., et al., "Neural networks in astronomy", Neural Networks 16 (2003) 297
>
>
  • 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
Changed:
<
<
  • Miller A.S., "A Review of Neural Network Applications in Astronomy", Vistas in Astronomy 36 141-161 (1993)
>
>
  • Miller A.S., A Review of Neural Network Applications in Astronomy, Vistas in Astronomy 36 141-161 (1993)
 
Changed:
<
<
  • Charbonneau P., "Genetic Algorithms in Astronomy and Astrophysics", ApJS 101 309 (1995)
>
>
  • Charbonneau P., Genetic Algorithms in Astronomy and Astrophysics, ApJS 101 309 (1995)
 
Changed:
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<
White Papers
>
>

White Papers

 
Changed:
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<
>
>
 
Changed:
<
<
>
>
 
Changed:
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<
Websites
>
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Websites

 

  • 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

Changed:
<
<
Blogs
>
>

Blogs

 

General Data Mining Links

Changed:
<
<
Books
>
>

Books

 
Changed:
<
<
  • 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)
>
>
  • 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)
 
Changed:
<
<
  • Gregory P., Bayesian Logical Data Analysis for the Physical Sciences (Cambridge University Press, Cambridge, UK, 2005)
>
>
  • Gregory P., Bayesian Logical Data Analysis for the Physical Sciences (Cambridge University Press, Cambridge, UK, 2005)
 
Changed:
<
<
  • Tufte, E.R., The Visual Display of Quantitative Information, 2nd edn. (Graphics Press, Cheshire, CT, 2001)
>
>
  • Tufte, E.R., The Visual Display of Quantitative Information, 2nd edn. (Graphics Press, Cheshire, CT, 2001)
 
Changed:
<
<
  • 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)
>
>
  • 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)
 
Changed:
<
<
Papers
>
>

Papers

 
Changed:
<
<
  • 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"
>
>
  • 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
 
Changed:
<
<
Websites
>
>

Websites

 
Changed:
<
<
Software
>
>

Software

 


-- NickBall - 19 Mar 2011
-- NickBall - 23 Sep 2011

Changed:
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<
-- NickBall - 02 Oct 2011
>
>
-- NickBall - 03 Oct 2011
 

<--  
-->

Revision 82011-10-03 - NickBall

 
META TOPICPARENT name="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

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
Changed:
<
<
  • 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. 91114 (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., PASA 28 150 (2011): "Scientific Visualization in Astronomy: Towards the Petascale Astronomy Era"

  • 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

Websites

  • 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
Added:
>
>
 
Changed:
<
<
>
>
Blogs
Deleted:
<
<
 
Added:
>
>
 
Added:
>
>
 

General Data Mining Links

Books

Changed:
<
<
  • 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).
>
>
  • 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)
 
Changed:
<
<
  • Gregory P., Bayesian Logical Data Analysis for the Physical Sciences, Cambridge University Press (2005)
>
>
  • Gregory P., Bayesian Logical Data Analysis for the Physical Sciences (Cambridge University Press, Cambridge, UK, 2005)
 
Changed:
<
<
  • Bishop C.M., Neural Networks for Pattern Recognition (Oxford University Press, Oxford, 1995).
>
>
  • Tufte, E.R., The Visual Display of Quantitative Information, 2nd edn. (Graphics Press, Cheshire, CT, 2001)
Deleted:
<
<
  • Ripley B.D., Pattern Recognition and Neural Networks (Cambridge University Press, Cambridge, UK, 2008).
 
Added:
>
>
  • 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)
 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

Software


-- NickBall - 19 Mar 2011
-- NickBall - 23 Sep 2011

Added:
>
>

-- NickBall - 02 Oct 2011
 
Changed:
<
<

>
>

 
<--  
-->

Revision 72011-09-24 - NickBall

 
META TOPICPARENT name="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

Review Papers

Added:
>
>
  • 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
  • Borne K., "Scientific Data Mining in Astronomy", Data Mining and Knowledge Discovery Series, Taylor & Francis: CRC Press, Boca Raton, FL, Ch. 5, pp. 91114 (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)
Deleted:
<
<
  • Hassan, A. & Fluke, C.J., "Scientific Visualization in Astronomy: Towards the Petascale Astronomy Era", PASA accepted, arXiv/1102.5123 (2011)
 
Added:
>
>
  • 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 (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

Websites

  • 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

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

Software


Changed:
<
<
-- NickBall - 04 Apr 2011
>
>
-- NickBall - 19 Mar 2011
Added:
>
>

-- NickBall - 23 Sep 2011
 

<--  
-->

Revision 62011-04-04 - NickBall

 
META TOPICPARENT name="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

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. 91114 (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)
Added:
>
>
  • Hassan, A. & Fluke, C.J., "Scientific Visualization in Astronomy: Towards the Petascale Astronomy Era", PASA accepted, arXiv/1102.5123 (2011)
 
Added:
>
>
 
  • 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

Websites

  • 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

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

Software


Changed:
<
<
-- NickBall - 19 Mar 2011
>
>
-- NickBall - 04 Apr 2011
 

<--  
-->

Revision 52011-03-19 - NickBall

 
META TOPICPARENT name="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

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. 91114 (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)

White Papers

Websites

  • 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

General Data Mining Links

Books

Changed:
<
<
  • 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).
>
>
  • 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).
 
Changed:
<
<
  • Gregory, P., Bayesian Logical Data Analysis for the Physical Sciences, Cambridge University Press (2005)
>
>
  • Gregory P., Bayesian Logical Data Analysis for the Physical Sciences, Cambridge University Press (2005)
 
Changed:
<
<
  • 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).
>
>
  • 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
Changed:
<
<
  • 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"
>
>
  • 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

Software


Changed:
<
<
-- NickBall - 18 Mar 2011
>
>
-- NickBall - 19 Mar 2011
 
Added:
>
>
 
<--  
-->

Revision 42011-03-19 - NickBall

 
META TOPICPARENT name="IvoaKDDguide"

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

8: Links: Books, papers, websites

Changed:
<
<
There are of course a huge number of books, papers, and websites about data mining. This section does not aim to exhaustively list everything, but to point to some of those that are most useful for astronomy. We therefore simply list each section in alphabetical order by author, or URL.
>
>
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.
 
Changed:
<
<

General

>
>

Links specific to Astronomy

 
Added:
>
>
Books

 Review Papers
Changed:
<
<
  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. 91114.
  3. R. Tagliaferri et al., Neural networks in astronomy, Neural Networks 16 (2003) 297.
>
>
  • 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. 91114 (2009); arXiv/0911.0505
  • Feigelson E.D., "Cross-disciplinary research in astronomy", Proceedings of Science (2010)
Added:
>
>
  • 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)
 
Changed:
<
<
Books
>
>
  • Tagliaferri R., et al., "Neural networks in astronomy", Neural Networks 16 (2003) 297
Added:
>
>
  • 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)
 
Changed:
<
<
  1. T. Hey, S. Tansley and K. Talle (eds.), The Fourth Paradigm: Data-Intensive Scientific Discovery (Microsoft Research, Redmond, WA, 2009).
>
>
  • Charbonneau P., "Genetic Algorithms in Astronomy and Astrophysics", ApJS 101 309 (1995)
Deleted:
<
<
  1. C. Kamath, Scientific Data Mining: A Practical Perspective (Society for Industrial and Applied Mathematics, Philadelphia, PA, 2009).
  2. T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer Series in Statistics, 2nd edn. (Springer, New York, 2009).
  3. 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).
  4. C. M. Bishop, Pattern Recognition and Machine Learning (Springer, New York, 2007).
 
Added:
>
>
White Papers

 Websites
Changed:
<
<
  1. http://www.kdnuggets.com
  2. http://www.kdd.org
>
>
Deleted:
<
<
  1. http://www.autonlab.org/tutorials
  2. http://en.wikipedia.org/wiki/Category:Machine_learning
  3. http://www.practicalastroinformatics.org/
 
Added:
>
>
  • 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
 
Changed:
<
<

Specific Methods

>
>
Added:
>
>
 
Changed:
<
<
  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).
>
>
 
Deleted:
<
<
...
 
Added:
>
>

General Data Mining Links

 
Changed:
<
<

Software

>
>
Books
 
Changed:
<
<
  1. http://www.cs.waikato.ac.nz/ml/weka/
  2. http://pcdevauc.na.infn.it:9000/
>
>
  • 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).
Added:
>
>
  • 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).
 
Changed:
<
<

>
>
  • Gregory, P., Bayesian Logical Data Analysis for the Physical Sciences, Cambridge University Press (2005)
 
Changed:
<
<
KDD-IG members: please add links here!
>
>
  • Bishop, C.M., Neural Networks for Pattern Recognition (Oxford University Press, Oxford, 1995).
Added:
>
>
  • Ripley, B.D., Pattern Recognition and Neural Networks (Cambridge University Press, Cambridge, UK, 2008).
 
Added:
>
>
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

Software

 
Changed:
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<
-- NickBall - 05 Sep 2010
>
>
-- NickBall - 18 Mar 2011
 
<--  
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Revision 32011-01-07 - NickBall

 
META TOPICPARENT name="IvoaKDDguide"

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

8: Links: Books, papers, websites

There are of course a huge number of books, papers, and websites about data mining. This section does not aim to exhaustively list everything, but to point to some of those that are most useful for astronomy. We therefore simply list each section in alphabetical order by author, or URL.

General

Review Papers

Changed:
<
<
  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
>
>
  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
 
  1. 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. 91114.
  2. 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).
Changed:
<
<
  1. 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. T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer Series in Statistics, 2nd edn. (Springer, New York, 2009).
Deleted:
<
<
New York, 2009).
 
  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).

...

Software

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


KDD-IG members: please add links here!


-- NickBall - 05 Sep 2010


<--  
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Revision 22010-09-09 - NickBall

 
META TOPICPARENT name="IvoaKDDguide"

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

8: Links: Books, papers, websites

Changed:
<
<
There are of course a huge number of books, papers, and websites about data mining. This section does not aim to exhaustively list everything, but to point to some of those that are most useful for astronomy.
>
>
There are of course a huge number of books, papers, and websites about data mining. This section does not aim to exhaustively list everything, but to point to some of those that are most useful for astronomy. We therefore simply list each section in alphabetical order by author, or URL.
Added:
>
>

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. 91114.
  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,
New York, 2009).
  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).

...

Software

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

KDD-IG members: please add links here!


-- NickBall - 05 Sep 2010


<--  
-->

Revision 12010-09-06 - NickBall

 
META TOPICPARENT name="IvoaKDDguide"

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

8: Links: Books, papers, websites

There are of course a huge number of books, papers, and websites about data mining. This section does not aim to exhaustively list everything, but to point to some of those that are most useful for astronomy.


KDD-IG members: please add links here!


-- NickBall - 05 Sep 2010


<--  
-->
 
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