Servicenavigation


Sie sind hier:

Data Mining Cup

Empfohlene Literatur für die Vorträge

Literatur zu Data Mining allgemein

  • C. M. Bishop. Pattern Recognition and Machine Learning. Information Science and Statistics. Springer, New York, NY, 2006. Zusatzmaterial
  • B. Clarke, E. Fokoué, and H. H. Zhang. Principles and Theory for Data Mining and Machine Learning. Springer Series in Statistics. Springer, New York, NY, 2009.
  • T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd edition, Springer Series in Statistics. Springer, New York, NY, 2009.

 

Exploration / Visualisierung

  • W. S. Cleveland, Visualizing Data, Hobart Press, Summit, NJ, 1993.
  • D. J. Hand, H. Mannila, and P. Smyth. Principles of Data Mining. The MIT Press, Cambridge, MA, 2001.
    Kapitel 3
  • A. Unwin, M. Theus, and H. Hofmann. Graphics of Large Datasets: Visualizing a Million. Statistics and Computing. Springer, New York, NY, 2006.
  • R-Pakete zur Visualisierung auf CRAN: beanplot, lattice, ggplot2, latticist, iplots, rgl, scatterplot3d
  • mondrian und weitere Software: http://rosuda.org/software/

 

Preprocessing

 

LDA, QDA, logistische Regression

  • C. M. Bishop. Pattern Recognition and Machine Learning. Information Science and Statistics. Springer, New York, NY, 2006. Zusatzmaterial
    Kapitel 4
  • L. Fahrmeir, W. Häußler, and G. Tutz. Diskriminanzanalyse. In L. Fahrmeir, A. Hamerle, and G. Tutz, editors, Multivariate Statistische Verfahren, chapter 8, pages 357–435. de Gruyter, Berlin, second edition, 1996. (ZB / Lehrbuchsammlung)
  • T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd edition, Springer Series in Statistics. Springer, New York, NY, 2009.
    Kapitel 4

 

Entscheidungsbäume und Random Forests

  • C. M. Bishop. Pattern Recognition and Machine Learning. Information Science and Statistics. Springer, New York, NY, 2006. Zusatzmaterial
    Kapitel 14.4
  • B. Clarke, E. Fokoué, and H. H. Zhang. Principles and Theory for Data Mining and Machine Learning. Springer Series in Statistics. Springer, New York, NY, 2009.
    Kapitel 5.3
  • L. Fahrmeir, W. Häußler, and G. Tutz. Diskriminanzanalyse. In L. Fahrmeir, A. Hamerle, and G. Tutz, editors, Multivariate Statistische Verfahren, chapter 8, pages 357–435. de Gruyter, Berlin, second edition, 1996. (ZB / Lehrbuchsammlung)
  • T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd edition, Springer Series in Statistics. Springer, New York, NY, 2001.
    Kapitel 9, 15.

 

Support Vector Machines

 

Vergleich von Methoden, Resampling, Tuning

 

Variablenselektion

 

Penalisierung und Shrinkage

  • B. Clarke, E. Fokoué, and H. H. Zhang. Principles and Theory for Data Mining and Machine Learning. Springer Series in Statistics. Springer, New York, NY, 2009.
    Kapitel 10.3

 

Klassifikation mit Kosten

Literatur und Vorträge zum Data Mining Cup


Data Mining Cup allgemein

 

Data Mining Cup 2010

 

Data Mining Cup 2009

 

Data Mining Cup 2008

 

Data Mining Cup 2007

 

Data Mining Cup 2004

 

GfKl Data Mining Competition 2005