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DTMN 2026: International Conference on Data Mining

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DTMN
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截稿日期:
2026-09-26 还有 2 天
通知日期:
2026-10-03
会议日期:
2026-10-17
会议地点:
Sydney, Australia
届数:
浏览: 17113   关注: 3   参加: 0

会伴指数 (CP-I)

51.9 / 100
全站第 1,211 名 / 共 5,684 个会议 · 前 22%

数据挖掘与数据库 第 62 / 337

学术认可 (35%) 无数据 —— 按中性基准 50 分计入 —
投稿选择性 (20%) 无数据 —— 按中性基准 50 分计入 —
会议传承 (20%)
69
社区关注 (10%)
24
资料公开度 (15%)
55

用到的输入: 有据可查的届次：12 · 在会伴关注它的研究者：3 人 · 过去 24 个月打开过本页的研究者：3 人

公开资料里还缺: 历年录用率 (+4.5) · 最佳论文记录 (+2.3)
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 45% —— 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 2026-09-24

征稿

DTMN 2026 (International Conference on Data Mining) is an academic conference held in Sydney, Australia on 2026-10-17. The paper submission deadline is 2026-09-26. Acceptance notifications are sent on 2026-10-03.

Scope & Topics 12th International Conference on Data Mining (DTMN 2026) provides a forum for researchers who address this issue and to present their work in a peer-reviewed forum. Authors are solicited to contribute to the conference by submitting articles that illustrate research results projects surveying works and industrial experiences that describe significant advances in Data mining and Applications. Authors are solicited to contribute to the conference by submitting articles that illustrate research results projects surveying works and industrial experiences. All submissions must describe original research not published or currently under review for another conference or journal. Topics of interest include, but are not limited to, the following: · Foundations of Data Mining · Large Scale, Distributed and Cloud Native Data Mining · Federated, On‑Device and Privacy Preserving Mining · Mining Text, Web, Graph, Social and Semi Structured Data · Spatio‑Temporal, Streaming and Real Time Mining · Multimedia and Multimodal Data Mining · Graph Mining, Network Science and Knowledge Graphs · Deep Learning, Representation Learning and Feature Engineering · Self‑Supervised, Contrastive and Semi Supervised Mining · Active Learning and Reinforcement Learning for Mining · Mining Foundation Model Outputs and LLM Behaviors · Mining Multimodal Foundation Models · LLM‑Driven and Autonomous Data Mining Pipelines · Agentic AI and Multi Agent Mining Systems · Generative AI for Data Mining · Causal Discovery and Causal Data Mining · Knowledge Discovery, Pattern Mining and Frequent Structures · Scientific ML, Symbolic Regression and Scientific Data Mining · Anomaly Detection, Outlier Analysis and Rare Event Mining · Personalization, Recommendation and User Modeling · Search, Ranking and Information Retrieval Mining · Security, Privacy, Fraud and Threat Intelligence Mining · Adversarial Data Mining and Robustness · IoT, Sensor Fusion and Cyber Physical Systems Mining · Autonomous Systems and Vehicle Data Mining · Edge‑Native and TinyML Driven Data Mining · Healthcare, Biological and Medical Data Mining · Climate, Environmental and Sustainability Data Mining · Financial, Economic and Business Data Mining · Social Media, Social Networks and Human Behavior Mining · Human Centric and Societal Scale Data Mining · Logs, Telemetry, Observability and AIOps Mining · Synthetic Data Generation and Augmentation · AutoML, Meta Learning and Automated Mining Pipelines · Explainable and Interpretable Data Mining · Fairness, Ethics, Bias and Responsible Data Mining · Safety Critical Data Mining and Risk Sensitive Analytics · Data Governance, Lineage and Quality Mining
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