posted by organizer: gmelli || 82163 views || tracked by 463 users: [display]

KDD 2016 : 22nd ACM SIGKDD international conference on knowledge discovery and data mining

FacebookTwitterLinkedInGoogle


Conference Series : Knowledge Discovery and Data Mining
 
Link: http://www.kdd.org/kdd2016/calls
 
When Aug 13, 2016 - Aug 17, 2016
Where San Francisco, USA
Submission Deadline Feb 12, 2016
Notification Due May 12, 2016
Final Version Due Jun 10, 2016
Categories    data science   machine learning   data mining   knowledge discovery
 

Call For Papers

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.

Related Resources

KDD 2019   25TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING
MLDM 2019   15th International Conference on Machine Learning and Data Mining MLDM 2019
ICMLA 2019   18th IEEE International Conference on Machine Learning and Applications
ICDMML 2019   【ACM ICPS EI SCOPUS】2019 International Conference on Data Mining and Machine Learning
ICCV 2019   International Conference on Computer Vision
RecSys 2019   13th ACM Conference on Recommender Systems
IEEE BigData 2019   IEEE International Conference on Big Data
COMML 2020   International Conference on Optimization, Metaheuristics and Machine Learning
PAKDD 2019   Pacific-Asia Conference on Knowledge Discovery and Data Mining
DSIS 2019   ISSAT International Conference on Data Science and Intelligent Systems