Conference Information

DTMN 2026: International Conference on Data Mining

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Submission Date:
2026-09-26
Notification Date:
2026-10-03
Conference Date:
2026-10-17
Location:
Sydney, Australia
Years:
Viewed: 17255   Tracked: 3   Attend: 0

Conference Partner Index (CP-I)

51.9 / 100
Ranked #1,227 of 5,693 conferences · Top 22%

#62 of 337 in Data Mining & Databases

Academic recognition (35%) No data - scored at the neutral baseline of 50 —
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%)
69
Community attention (10%)
24
Public record completeness (15%)
55

Inputs used: Editions on record: 12 · Researchers following it here: 3 · Researchers who opened this page in the past 24 months: 3

Missing from the public record: Historical acceptance rates (+4.5) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 45% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-10-05

Call For Papers

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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