01
Foundations & Theories
Foundations, algorithms, models, and theory of data mining, including big data mining
02
ML/DL & Statistical Methods
Machine learning, deep learning, and statistical methods for big data
03
Heterogeneous Data Mining
Mining heterogeneous data sources, including text, semi-structured, spatio-temporal, streaming, graph, web, and multimedia data
04
Systems & Platforms
Data mining systems and platforms for analyzing big data, including methods for parallel and distributed data mining, federated learning, and their efficiency, scalability, security, and privacy
05
Modeling, Visualization & Recommendations
Data mining for modeling, visualization, personalization, and recommendation
06
CPS & Complex Time-Evolving Networks
Data mining for cyber-physical systems and complex, time-evolving networks
07
LLM-Driven
Data mining with large language models
08
Novel Applications
Novel applications of data mining in data science, including big data analysis in social sciences, physical sciences, engineering, life sciences, climate science, web, marketing, finance, precision medicine, health informatics, and other domains