SIAM International Conference on Data Mining (SDM26)

Submission Deadlines are due 11:59 p.m. Anywhere on Earth (AoE)

About the Conference

This meeting is sponsored by the SIAM Activity Group on Data Science. 

The SIAM Data Mining (SDM) conference invites submissions of high-quality research papers that present original results on data mining algorithms and their applications. Data mining is a core process within computing and statistics, aimed at discovering valuable knowledge from data. This field has significant applications across various domains including science, engineering, healthcare, business, and medicine. Datasets in these fields are typically large, complex, and noisy, necessitating sophisticated, high-performance analysis techniques grounded in sound theoretical and statistical principles. The SDM conference provides a venue for researchers who are addressing these problems to present their work in a peer-reviewed forum. It also provides an ideal setting for graduate students to network and get feedback for their work (as part of the doctoral forum) and everyone new to the field to learn about cutting-edge research by hearing outstanding invited speakers and attending presentations, tutorials and a number of focused workshops. The proceedings of the conference are published in archival form and are also made available on the SIAM Web site.

The following meetings will be held jointly:
SIAM Conference on Imaging Science (IS26)
SIAM Conference on Mathematics of Data Science (MDS26)
SIAM International Conference on Data Mining (SDM26)

Connect with other attendees on LinkedIn.

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