IEEE International Conference on Data Mining ICDM 2019

Start:
2019/11/08
End:
2019/11/11
Location:
Beijing, China
Link:
IEEE International Conference on Data Mining
Description:

Aims and Scope

The IEEE International Conference on Data Mining (ICDM) has established itself as the world’s premier research conference in data mining. It provides an international forum for presentation of original research results, as well as exchange and dissemination of innovative and practical development experiences. The conference covers all aspects of data mining, including algorithms, software, systems, and applications. ICDM draws researchers, application developers, and practitioners from a wide range of data mining related areas such as statistics, machine learning, pattern recognition, databases, data warehousing, data visualization, knowledge-based systems, and high-performance computing. By promoting novel, high-quality research findings, and innovative solutions to challenging data mining problems, the conference seeks to advance the state-of-the-art in data mining.

Topics of Interest

Topics of interest include, but are not limited to:

  • Foundations, algorithms, models and theory of data mining, including big data mining.
  • Machine learning and statistical methods for data mining.
  • Mining from heterogeneous data sources, including text, semi-structured, spatio-temporal, streaming, graph, web, and multimedia data.
  • Data mining systems and platforms, and their efficiency, scalability, security and privacy.
  • Data mining for modelling, visualization, personalization, and recommendation.
  • Data mining for cyber-physical systems and complex, time-evolving networks.
  • Applications of data mining in social sciences, physical sciences, engineering, life sciences, web, marketing, finance, precision medicine, health informatics, and other domains.

We particularly encourage submissions in emerging topics of high importance such as data quality, time-evolving networks, big data mining and analytics, cyber-physical systems, and heterogeneous data integration and mining.