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KDD 2009 - Paris   

15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Paris 2009

The annual ACM SIGKDD conference is the premier international forum for data mining researchers and practitioners from academia, industry, and government to share their ideas, research results and experiences. KDD-09 will feature keynote presentations, oral paper presentations, poster sessions, workshops, tutorials, panels, exhibits, demonstrations, and the KDD Cup competition.

External links:

KDD 2009 website
KDD webiste
ACM website


Event sections

Research Track Industrial Track Tutorials  

Invited talks

ACM SIGKDD Award Presentations

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flagKDD-09 Opening Session and Awards PresentationsKDD-09 Opening Session and Awards Presentations
Wei Wang, Jure Leskovec, et al. Wei Wang, Jure Leskovec, Bamshad Mobasher, Gabor Melli, David Vogel, Robert Grossman, Mohammed J. Zaki, Peter A. Flach, Françoise Fogelman Soulié

SIGKDD Annual Business Meeting

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flagSIGKDD Annual Progress ReportSIGKDD Annual Progress Report
Gregory Piatetsky-Shapiro, Jiawei Han Gregory Piatetsky-Shapiro, Jiawei Han
comments1 comment 

Reviews and comments:

Comment1 Martin Rosas, June 10, 2010 at 11:39 a.m.:

I enjoyed all conferences, however specifically I would like to ask to Usama Fayyad and to Dr Grossman, wht do you thoink about to use the error of varianza instead of the model variance to approach the question problem because of the error variance is more consistent and I am not sure that this part needs to many adjustment that usually we use to turn countinuous nonrectilinear variables in @forced rectininear voariables to be submmited in a linear regression model. In a multivariable analsis we fit de model variance depending on the number of variables using different kind of conditional *Akai, Kendall etc* why not make data mining of residual instead of modelling test_-_

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