Data Mining and Big Data Analytics Technical Committee
Under the supervision and the coordination of the IEEE CIS Technical Activities Committee, The Data Mining and Big Data Analytics Technical Committee (DMTC) is established to: (1) promote the research, development, education and understanding the principles and applications of data mining and big data analytics and (2) to help researchers whose background is primarily in computational intelligence in increasing their contributions to this area.
Current directory of officers and members: Data Mining and Big Data Analytics Technical Committee Members.
Funding for the committee shall be provided by the CIS in the annual budget request. Once approved by the CIS ExCom and AdCom, funds will be available to support the DMTC activities. The Chair shall be responsible for the appropriate use of the approved funds with supporting documentation.
The DMTC shall engage in various activities in order to advance the goals described above, including but not limited to the following: Identify and promote new areas of research, propose special sessions to the CIS-sponsored conference organizers, publicize success stories on solving real data mining problems, participate in paper review and selection for CIS-sponsored conferences and publications, recommend candidates/papers for awards and collaborate on production of tutorials and book series with the Multimedia Committee. The DMTC will also assist in soliciting proposals for focused workshops or special sessions and actively work with the organizers of CIS-sponsored conferences to ensure their technical excellence.
Meetings will be called by the Chair once or twice a year, usually in conjunction with the CIS AdCom meetings or as requested by the DMTC members. Sufficient advance notice of the meetings will be given to the members, as well as to other interested parties. The Chair shall prepare an agenda before the meeting, and shall prepare minutes for distribution after the meeting.
This Charter becomes official after at least 2/3 core members present at The Committee Meeting approves it. Subsequent changes and amendments also require 2/3 majority vote.
- Big Data, Chair: Yonghong Peng, Co-Chairs: Nitesh Chawla, Paulo Lisboa
- Data Mining in Industrial Applications, Chair: Yi Lu Murphey, Vice Chairs: Thomas A. Montgomery, Mahmoud Abou-Nasr
- Data Science and Advanced Analytics, Chair: Longbin Cao, Vice Chairs: Eric Gaussier, Bart Goethals, George Karypis, Vincent S. Tseng
- Data Visualization and Data Analysis, Chair: Barbara Hammer, Vice Chairs: Laurens van der Maaten, Daniel Keim
- Educational Data Mining, Chair: Guandong Xu
- Evaluation and Quality, Chair: Philippe Lenca, Vice Chair: Stephane Lallich
- Medical Data Analysis, Chair: Alfredo Vellido, Vice Chairs: Paulo Lisboa, José D. Martín
- Mining Complex Astronomical Data, Chair: Erzsébet Merényi, Vice Chairs: Peter Tino, Pablo Huijse
- Process Mining, Chair: Wil van der Aalst
- Granular Data Mining for Big Data, Chair: Weiping Ding, Vice Chairs: Witold Pedrycz, Zehong Cao
- Reservoir Computing, Chair: Claudio Gallicchio, Vice Chairs: Alessio Micheli
- Explainable Machine Learning, Chair: Anna Wilbik, Vice Chairs: Paulo Lisboa, Qi Chen