IEEE International Conference on Data Mining

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Aims and Scope 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 sharing original research results, as well as exchanging and disseminating 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, high-performance computing, and large models. 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 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. Advantages and potential limitations of data mining with large models. Applications of data mining in social sciences, physical sciences, engineering, life sciences, climate science, 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. Manuscripts must be submitted electronically through the online submission system: https://wi-lab.com/cyberchair/2026/icdm26/scripts/submit.php?subarea=DM Paper should be limited to a maximum of ten (10) pages, in the IEEE 2-column format, including the references 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. 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 references must be referenced to preserve anonymity. Additionally, authors should refrain from uploading or publicizing their submissions on preprint servers (e.g., arXiv) or other online platforms during the review period. Preprints that were posted before submission may remain online and do not need to be removed. Accepted papers will be published in the conference proceedings by the IEEE Computer Society Press. All manuscripts must be submitted as full papers and will be reviewed based on their scientific merit. There is no separate abstract submission deadline, and submissions are not categorized as long or short papers at the time of submission. After acceptance, papers will be designated as Regular or Short papers, with limits of ten pages.

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