IEEE Symposium on Computational Intelligence in Data Mining (CIDM)

IEEE CIDM 2021 organized by the IEEE Computational Intelligence Society Data Mining Technical Committee is one of the largest and best attended symposia of the of the IEEE Symposium Series of Computational Intelligence (IEEE SSCI 2021). IEEE CIDM 2021 will bring together researchers and practitioners from around the world to discuss the latest advances in the field of computational intelligence applied to data mining and will act as a major forum for the presentation of recent results in theory, algorithms, systems and applications.


Topics related to all aspects of data mining and machine learning, such as theories, algorithms, systems and applications, particularly those based on computational intelligence technologies, are welcome; these include, but are not limited to:

  • Neural networks for data mining
  • Evolutionary algorithms for data mining
  • Fuzzy sets for data mining
  • Data mining with soft computing
  • Foundations of data mining
  • Mining with big data
  • Classification, Clustering, Regression
  • Association
  • Feature learning and feature engineering
  • Machine learning algorithms
  • Mining from streaming data
  • Deep learning
  • Data mining from nonstationary and drifting environments
  • Multimedia data mining
  • Text mining
  • Link and graph mining
  • Social media mining
  • Collaborative filtering
  • Crowd sourcing
  • Personalization
  • Security, privacy and social impact of data mining
  • Data mining applications

Symposium Chairs

Zhen Ni, Florida Atlantic University
Simone Ludwig, North Dakota State University

Bach Nguyen Hoai, Victoria University of Wellington

Programme Committee

Tufan Kumbasar, Istanbul Technical University
Wil van der Aalst, Eindhoven University of Technology
Sansanee Auephanwiriyakul, Chiang Mai University
Ahmad Taher Azar, Benha University
Giacomo Boracchi, Politecnico di Milano
Qi Chen, Victoria University of Wellington
Keeley Crockett, Manchester Metropolitan University
Weiping Ding, Nantong University
Gregory Ditzler, The University of Arizona
Haibo He, University of Rhode Island
Bach Nguyen Hoai, Victoria University of Wellington
Ting Hu, Queen’s University
Yonghong (Catherine) Huang, McAfee AI Research
Ata Kaban, University of Birmingham

Gang Li, Deakin University
Yun Li, i4AI Ltd
Jane Jing Liang, Zhengzhou University
Simone Ludwig, North Dakota State University
Paulo Lisboa, Liverpool John Moores University
Patricia Melin, Tijuana Institute of Technology
Sanaz Mostaghim, Otto von Guericke University of Magdeburg
Su Nguyen, Hoa Sen University
Yonghong Peng, University of Sunderland
Robi Polikar, Rowan University
Kai Alex Qin, Swinburne University of Technology
Marek Reformat, University of Alberta, Canada
Manuel Roveri, Politecnico di Milano
Antonio Tallon
Alfredo Vellido, Universitat Politècnica de Catalunya (UPC BarcelonaTech)
Handing Wang, Xidian University
Lipo Wang, Nanyang Technological University
Anna M. Wilbik, Eindhoven University of Technology
Guandong Xu, University of Technology Sydney
Gary G. Yen, Oklahoma State University
Yang Yu, Nanjing University
Zhiwen Yu, South China University of Technology
Mengjie Zhang, Victoria University of Wellington
Dongbin Zhao, Institute of Automation, Chinese Academy of Sciences