Big Data Mining and Analytics

The “Big Data Mining and Analytics” Section is dedicated to research on knowledge discovery from large-scale, heterogeneous, multimodal, dynamic, or distributed data. It provides an interdisciplinary forum for new foundations, algorithms, systems, evaluation methods, datasets, and high-impact applications in data mining.

The Section welcomes original research articles, reviews, perspectives, and application-oriented studies. Contributions may address data preparation, pattern discovery, predictive modeling, scalable mining, model evaluation, or knowledge extraction. Particular attention will be given to efficient, interpretable, privacy-preserving, fair, and robust methods. Reproducible studies, benchmark datasets, open-source tools, and rigorously evaluated applications are also encouraged. The Section aims to connect methodological innovation with practical impact by promoting new analytical foundations, reliable systems, and rigorous knowledge discovery in real-world settings.

Topics include, but are not limited to, the following areas:

• Knowledge discovery;
• Classification;
• Clustering;
• Dimensionality reduction;
• Frequent pattern mining;
• Anomaly detection;
• Graph mining;
• Temporal data mining;
• Spatial data mining;
• Stream mining;
• Web mining;
• Text mining;
• Multimedia mining;
• Social network mining;
• Recommender systems;
• User behavior modeling;
• Topic discovery;
• Opinion mining;
• Knowledge graph construction;
• Data integration;
• Data cleaning;
• Data summarization;
• Scalable data mining;
• Distributed data mining;
• Privacy-preserving data mining;
• Explainable data mining;
• Fairness in data mining;
• Robust data mining;
• Large language models for data mining;
• Benchmark datasets;
• Data mining evaluation;
• Image mining;
• Video mining;
• Visual data mining;
• Multimodal data mining;
• Cross-modal retrieval.