
November 12 - November 15, 2025
IEEE International Conference on Data Mining
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Overview
The IEEE International Conference on Data Mining (ICDM) is a premier research conference providing an international forum for sharing original research results and exchanging innovative development experiences in data mining. ICDM 2025 will be held in Washington DC, USA, from November 12-15, 2025. The conference covers all aspects of data mining, including algorithms, software, systems, and applications, and draws researchers, application developers, and practitioners from various data mining-related areas.
IEEE ICDM 2025: Call for Papers
The 25th IEEE International Conference on Data Mining (ICDM) will be held in Washington DC, USA, November 12-15, 2025.
ICDM is a premier research conference providing an international forum for sharing original research results and exchanging innovative development experiences in data mining. The conference covers all aspects of data mining, including algorithms, software, systems, and applications.
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, deep learning, and statistical methods for big data.
- Mining heterogeneous data sources, including text, semi-structured, spatio-temporal, streaming, graph, web, and multimedia data
- Data mining systems and platforms for analyzing big data, including methods for parallel and distributed data mining, federated learning, 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
- Data mining with large language models
- Novel applications of data mining in data science, including big data analysis 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.
Important Dates
Event | Deadline |
---|---|
Full paper submissions | June 6, 2025 |
Notifications to authors | August 25, 2025 |
Camera ready | September 25, 2025 |
Workshop proposals deadline | March 14, 2025 |
Workshop proposals notification | April 11, 2025 |
Workshop paper submission deadline | September 1, 2025 |
Tutorial proposal due | September 5, 2025 |
Tutorial notification | September 26, 2025 |
Demo paper submission | September 5, 2025 |
Demo notification | September 26, 2025 |
REU Paper submission | September 1, 2025 |
REU Decision notification | October 1, 2025 |
REU Camera-ready | October 15, 2025 |
UGH Submission Deadline | September 1, 2025 |
UGH Notification of Acceptance | October 1, 2025 |
UGH Camera-Ready Submission | October 15, 2025 |
All submission deadlines are end-of-day in the Anywhere on Earth (AoE) time zone.
Additional Information
- Hotel Room Block Cut-Off Date: October 18, 2025. book.passkey.com/go/IEEE25
Conference Dates
Conference Date
November 12, 2025 → November 15, 2025
Submission
(Workshops) Workshop proposals deadline
March 14, 2025
(Main conference) Full paper submissions
June 6, 2025
(REU Symposium) Paper submission due date
September 1, 2025
Notification
(Workshops) Workshop proposals notification
April 11, 2025
(Main conference) Notifications to authors
August 25, 2025
(Demonstrations) Notification to authors
September 26, 2025
Camera-Ready
(Main conference) Camera ready
September 25, 2025
(Workshops) Camera ready
September 25, 2025
(REU Symposium) Camera-ready due date
October 15, 2025
Other Dates
Room Block Cut-Off Date
October 18, 2025
(REU Symposium) Symposium
November 12, 2025 → November 15, 2025
Source Rank
Source: CORE2023
Rank: A*
Field of Research: Data management and data science, Machine learning