KDD: Knowledge Discovery and Data Mining

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Past:   Proceedings on DBLP

Future:  Post a CFP for 2017 or later   |   Invite the Organizers Email

 
 

All CFPs on WikiCFP

Event When Where Deadline
KDD 2016 22nd ACM SIGKDD international conference on knowledge discovery and data mining
Aug 13, 2016 - Aug 17, 2016 San Francisco, USA Feb 12, 2016
KDD 2015 21th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Aug 10, 2015 - Aug 13, 2015 Sydney, Australia Feb 20, 2015
KDD 2014 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Aug 24, 2014 - Aug 27, 2014 New York, USA Feb 21, 2014 (Feb 13, 2014)
KDD 2013 ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Aug 11, 2013 - Aug 14, 2013 Chicago, USA Feb 22, 2013 (Feb 15, 2013)
KDD 2012 18th ACM SIGKDD Knowledge Discovery and Data Mining
Aug 12, 2012 - Aug 16, 2012 Beijing, China Feb 10, 2012
KDD 2011 The 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Aug 21, 2011 - Aug 24, 2011 San Diego, California Feb 18, 2011 (Feb 11, 2011)
KDD 2010 The 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Jul 25, 2010 - Jul 28, 2010 Washington DC, USA Feb 5, 2010 (Feb 2, 2010)
KDD 2009 The 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Jun 28, 2009 - Jul 1, 2009 Paris, France Feb 6, 2009 (Feb 2, 2009)
KDD 2008 The 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Aug 24, 2008 - Aug 27, 2008 Las Vegas, USA Feb 29, 2008 (Feb 22, 2008)
 
 

Present CFP : 2016

KDD 2016, a premier interdisciplinary conference, brings together researchers and practitioners from data science, data mining, knowledge discovery, large-scale data analytics, and big data.

We invite submission of papers describing innovative research on all aspects of knowledge discovery and data mining, ranging from theoretical foundations to novel models and algorithms for data mining problems in science, business, medicine, and engineering. Visionary papers on new and emerging topics are also welcome, as are application-oriented papers that make innovative technical contributions to research. Authors are explicitly discouraged from submitting incremental results that do not provide significant advances over existing approaches.
 

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