Conference Information
ICDM 2021: International Conference on Data Mining
https://icdm2021.auckland.ac.nz/
Submission Date:
2021-06-11
Notification Date:
2021-08-31
Conference Date:
2021-12-07
Location:
Auckland, New Zealand
Years:
21
CCF: b   CORE: a*   QUALIS: a1   Viewed: 121902   Tracked: 354   Attend: 55
Conference Location
Call For Papers
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 big data, deep learning, pattern recognition, statistical and machine learning,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.
    Deep 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 modelling, 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 ethical data analytics, automated data analytics, data-driven reasoning, interpretable modeling, modeling with evolving environment, cyber-physical systems, multi-modality data mining, and heterogeneous data integration and mining.
Last updated by Ping Zhang in 2021-04-23
Acceptance Ratio
YearSubmittedAcceptedAccepted(%)
201472714219.5%
201380915919.7%
201275615120%
200759211920.1%
200677615219.6%
200550114128.1%
2004451398.6%
20035015811.6%
200236912132.8%
20013657219.7%
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Related Journals
CCFFull NameImpact FactorPublisherISSN
bData & Knowledge Engineering1.476Elsevier0169-023X
aIEEE Transactions on Knowledge and Data Engineering3.857IEEE1041-4347
Journal of Digital Imaging2.572Springer0897-1889
cJournal of Database Management2.121IGI Global1063-8016
Spatial Statistics1.656Elsevier2211-6753
bData Mining and Knowledge Discovery3.16Springer1384-5810
Crisis Communications Springer2194-9794
The Photogrammetric Record1.591Wiley-Blackwell0031-868X
Journal of Digital Learning in Teacher EducationTaylor & Francis2153-2974
Statistical Analysis and Data Mining John Wiley & Sons, Ltd1932-1872
Full NameImpact FactorPublisher
Data & Knowledge Engineering1.476Elsevier
IEEE Transactions on Knowledge and Data Engineering3.857IEEE
Journal of Digital Imaging2.572Springer
Journal of Database Management2.121IGI Global
Spatial Statistics1.656Elsevier
Data Mining and Knowledge Discovery3.16Springer
Crisis Communications Springer
The Photogrammetric Record1.591Wiley-Blackwell
Journal of Digital Learning in Teacher EducationTaylor & Francis
Statistical Analysis and Data Mining John Wiley & Sons, Ltd
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