
12 novembre - 15 novembre 2025
IEEE International Conference on Data Mining
Aucun abonné pour le moment.
Aperçu
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
Dates de la conférence
Conference Date
12 novembre 2025 → 15 novembre 2025
Soumission
(Workshops) Workshop proposals deadline
14 mars 2025
(Main conference) Full paper submissions
6 juin 2025
(REU Symposium) Paper submission due date
1 septembre 2025
Notification
(Workshops) Workshop proposals notification
11 avril 2025
(Main conference) Notifications to authors
25 août 2025
(Demonstrations) Notification to authors
26 septembre 2025
Version finale
(Main conference) Camera ready
25 septembre 2025
(Workshops) Camera ready
25 septembre 2025
(REU Symposium) Camera-ready due date
15 octobre 2025
Autres dates
Room Block Cut-Off Date
18 octobre 2025
(REU Symposium) Symposium
12 novembre 2025 → 15 novembre 2025
Classement source
Source: CORE2023
Classement: A*
Domaine de recherche: Data management and data science, Machine learning