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ICDM 2024 : IEEE International Conference on Data Mining

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Conference Series : International Conference on Data Mining
 
Link: http://18.133.154.107/call_for_papers/
 
When Dec 9, 2024 - Dec 12, 2024
Where Abu Dhabi, UAE
Submission Deadline Jul 1, 2024
Notification Due Sep 1, 2024
Final Version Due Oct 15, 2024
Categories    data mining   deep learning   machine learning   data warehousing
 

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 environments, multi-modal data mining, and heterogeneous data integration and mining.
Submission Guidelines

Authors are invited to submit original papers, which have not been published elsewhere and which are not currently under consideration for another journal, conference or workshop.

Paper submissions should be limited to a maximum of ten (10) pages, in the IEEE 2-column format ( https://www.ieee.org/conferences/publishing/templates.html), including the bibliography and any possible appendices. Submissions longer than 10 pages will be rejected without review. All submissions will be triple-blind reviewed by the Program Committee on the basis of technical quality, relevance to scope of the conference, originality, significance, and clarity. The following sections give further information for authors.
Triple-blind submission guidelines

Since 2011, ICDM has imposed a triple-blind submission and review policy for all submissions. Authors must hence not use identifying information in the text of the paper and bibliographies must be referenced to preserve anonymity. Any papers available on the Web (including arXiv) no longer qualify for ICDM submissions, as their author information is already public.
What is triple-blind reviewing?

The traditional blind paper submission hides the referee names from the authors, and the double-blind paper submission also hides the author names from the referees. The triple-blind reviewing further hides the referee names among referees during paper discussions before their acceptance decisions. The names of authors and referees remain known only to the PC Co-Chairs, and the author names are disclosed only after the ranking and acceptance of submissions are finalized. It is imperative that all authors of ICDM submissions conceal their identity and affiliation information in their paper submissions. It does not suffice to simply remove the author names and affiliations from the first page, but also in the content of each paper submission.

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