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ICDM 2026: International Conference on Data Mining

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ICDM
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截稿日期:
2026-06-06
通知日期:
2026-08-16
会议日期:
2026-11-12
会议地点:
Shenyang, China
届数:
26
CCF: B   ICORE: A*   QUALIS: A1   浏览: 1866521   关注: 504   参加: 101

会伴指数 (CP-I)

94.1 / 100
全站第 15 名 / 共 5,650 个会议 · 前 1%

数据挖掘与数据库 第 3 / 336

学术认可 (35%)
100
投稿选择性 (20%)
100
会议传承 (20%)
89
社区关注 (10%)
93
资料公开度 (15%)
80

用到的输入: 收录等级：CCF B, QUALIS A1 · 录用率：9.4%（有记录的 5 届的均值） · 有据可查的届次：26 · 在会伴关注它的研究者：504 人 · 过去 24 个月打开过本页的研究者：163 人

公开资料里还缺: 历届信息 (+3.0)
主办方认领本会议后，可直接在这一页补上；分数每晚重算。如何提升这个分数

置信度 100% —— 分数中有多大比例来自实际观测到的数据，而不是中性基准。 这个分数是怎么算出来的 · 查看完整榜单 · 算法版本 1.1 · 算于 2026-09-04

征稿

ICDM 2026 (International Conference on Data Mining) is a CCF B / ICORE A* / QUALIS A1 conference held in Shenyang, China on 2026-11-12. The paper submission deadline is 2026-06-06. Acceptance notifications are sent on 2026-08-16.

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 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.
由 Dou Sun 最后更新于

录用率

平均录用率: 15.5% 17 年间 (2001–2021).

年份提交数录用数录用率(%)
2021990989.9%
2020930919.8%
20191046959.1%
2018948848.9%
2017778729.3%
2016904788.6%
2015807688.4%
201472714219.5%
201380915919.7%
201275615120%
200759211920.1%
200677615219.6%
200550114128.1%
2004451398.6%
20035015811.6%
200236912132.8%
20013657219.7%

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