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