ICDM: International Conference on Data Mining



Past:   Proceedings on DBLP

Future:  Post a CFP for 2019 or later   |   Invite the Organizers Email


All CFPs on WikiCFP

Event When Where Deadline
ICDM 2018 IEEE International Conference on Data Mining
Nov 17, 2018 - Nov 20, 2018 Singapore Jun 5, 2018
ICDM 2017 IEEE International Conference on Data Mining 2017
Nov 18, 2017 - Nov 21, 2017 NEW ORLEANS, USA Jun 5, 2017
ICDM 2016 The IEEE International Conference on Data Mining
Dec 12, 2016 - Dec 15, 2016 Barcelona, Spain Jun 17, 2016
ICDM 2014 IEEE International Conference on Data Mining
Dec 14, 2014 - Dec 17, 2014 Shenzhen, China Jun 24, 2014
ICDM 2013 IEEE International Conference on Data Mining
Dec 8, 2013 - Dec 11, 2013 Dallas, Texas, USA Jun 21, 2013
ICDM 2012 IEEE International Conference on Data Mining
Dec 10, 2012 - Dec 13, 2012 Brussels / Belgium Jun 18, 2012
ICDM 2010 The 10th IEEE International Conference on Data Mining
Dec 13, 2010 - Dec 17, 2010 Sydney, Australia Jul 2, 2010
ICDM 2009 The 2009 IEEE International Conference on Data Mining
Dec 6, 2009 - Dec 9, 2009 Miami, FLorida ,USA Jun 26, 2009
ICDM 2008 The 8th IEEE International Conference on Data Mining
Dec 15, 2008 - Dec 19, 2008 Pisa, Italy Jul 7, 2008

Present CFP : 2018

The IEEE International Conference on Data Mining (ICDM) has established itself as the world’s premier research conference in data mining. It provides an international forum for presentation of original research results, as well as exchange and dissemination of innovative and practical development experiences. The conference covers all aspects of data mining, including algorithms, software, systems, and applications. ICDM draws researchers, application developers, and practitioners from a wide range of data mining related areas such as statistics, machine learning, pattern recognition, databases, data warehousing, data visualization, knowledge-based systems, and high-performance computing. By promoting novel, high-quality research findings, and innovative solutions to challenging data mining problems, the conference seeks to advance the state-of-the-art in data mining.

Topics of Interest
Topics of interest include, but are not limited to:

Foundations, algorithms, models and theory of data mining, including big data mining.
Machine learning and statistical methods for data mining.
Mining from heterogeneous data sources, including text, semi-structured, spatio-temporal, streaming, graph, web, and multimedia data.
Data mining systems and platforms, and their efficiency, scalability, security and privacy.
Data mining for modeling, visualization, personalization, and recommendation.
Data mining for cyber-physical systems and complex, time-evolving networks.
Applications of data mining in social sciences, physical sciences, engineering, life sciences, web, marketing, finance, precision medicine, health informatics, and other domains.

We particularly encourage submissions in emerging topics of high importance such as data quality, time-evolving networks, big data mining and analytics, cyber-physical systems, and heterogeneous data integration and mining.

To see the full CFP: http://icdm2018.org/calls/call-for-papers/

Related Resources

IEEE ICDM 2019   ICDM 2019: The 19th IEEE International Conference on Data Mining
ICML 2020   37th International Conference on Machine Learning
ICDM 2019   19th Industrial Conference on Data Mining ICDM 2019
IWUAS 2020   2020 International Workshop on Unmanned Aircraft Systems (IWUAS 2020)
Data Science 2020   3nd Annual International Great Lakes Data Science Symposium
ICPR 2020   International Conference on Pattern Recognition 2020
ISBDAI 2020   【Ei Compendex Scopus】2018 International Symposium on Big Data and Artificial Intelligence
IEEE COINS 2020   Internet of Things IoT | Artificial Intelligence | Machine Learning | Big Data | Blockchain | Edge & Cloud Computing | Security | Embedded Systems | Circuit and Systems | WSN | 5G
CISDM 2020   2020 2nd European Conference on Information System and Data Mining (CISDM 2020)
Journal Special Issue 2019   Machine Learning on Scientific Data and Information