Conference Information

SDM 2026: SIAM International Conference on Data Mining

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Submission Date:
2026-04-10 Closed
Notification Date:
Conference Date:
2026-11-19
Location:
Salt Lake City, Utah, USA
Years:
26
CCF: B   ICORE: A   QUALIS: A2   Viewed: 280643   Tracked: 205   Attend: 24
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Conference Partner Index (CP-I)

86.2 / 100
Ranked #123 of 5,771 conferences · Top 3%

#7 of 340 in Data Mining & Databases

Academic recognition (35%)
92
Submission selectivity (20%)
97
Editions held (20%)
89
Community attention (10%)
71
Public record completeness (15%)
65

Inputs used: Listed as CCF B, ICORE A, QUALIS A2 · Acceptance rate: 14.5% (mean of 4 editions on file) · Editions on record: 26 · Researchers following it here: 205 · Researchers who opened this page in the past 24 months: 12

Missing from the public record: Past editions (+3.0) · 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 100% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.2 · Computed 2026-11-09

Call For Papers

SDM 2026 (SIAM International Conference on Data Mining) is a CCF B / ICORE A / QUALIS A2 conference held in Salt Lake City, Utah, USA on 2026-11-19. The paper submission deadline is 2026-04-10.

Topics of Interest: We welcome contributions addressing all aspects of data mining, machine learning, and visual analytics, including but not limited to: Included Themes Methods and Algorithms Anomaly & outlier detection Big data & large-scale systems Causal inference Classification & semi-supervised learning Clustering & unsupervised learning Data cleaning & integration Datasets & benchmarks Deep learning & representation learning Generative artificial intelligence (AI), foundation models, & agentic AI Knowledge-guided and physics-informed machine learning Mining data streams Mining graphs & complex data Mining on emerging architectures & data clouds Mining spatial & temporal data Mining text, web & social media Optimization methods Parallel & distributed methods Probabilistic & statistical methods Scalable & high-performance mining Self-supervised learning & reinforcement learning Transfer learning, continual learning, lifelong learning, & open-ended learning Visualization & interactive analytics Applications of Data Mining AI for Science (climate science, hydrology, neuroscience, cognitive science, material science, life sciences, chemistry, physics, engineering, etc.) Business & marketing Healthcare & bioinformatics Scientific hypothesis generation & validation Human Factors and Social Issues Ethics of data mining Intellectual ownership Interpretable, explainable, & trustworthy AI Privacy & fairness models Privacy preserving data mining Risk analysis & risk management Transparency & algorithmic bias
Last updated by Dou Sun on

Acceptance Ratio

Average acceptance rate: 14.5% over 4 years (2002–2008).

YearSubmittedAcceptedAccepted(%)
20082824014.2%
20073023611.9%
20052184018.3%
20022503413.6%

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