Visual data mining research seeks to enhance the knowledge discovery process using graphical representations of data mining results and processes and through the combination of visual and computational approaches to data exploration. By leveraging human perceptions of the visual space, patterns that might not otherwise be discovered will be identified using visual data mining techniques. Visual data mining utilizes concepts from a wide variety of disciplines, including: computer graphics, information visualization, knowledge discovery, cognition and visual perception.
Papers on all aspects of visual data mining and analytics are solicited. Papers will be refereed and appear in the conference proceedings, which will be published by official publisher of the conference..
A selection of the best papers will be recommended for publication in special issues of scientific journals, or as an edited book.
Topics of interest:
- Combining visual and computational methods of data analysis
- Visual querying
- Visual analytics of spatial, temporal, and spatio-temporal data
- Knowledge construction and management in visual analytics
- Privacy issues in visual analytics
- Cognitive approaches and explanations for visual data mining
- Visualization of data mining algorithm
- Scalability issues
- Empirical studies of performance
- Evaluation of visual data mining methods
- Collaborative visualization and mining
- Applications of visual data mining and analytics
- Case studies
- Computational steering for long-running data mining applications
- Reviews and surveys of related literature
Please check the submission procedures @ the submission page.
General enquiries and submissions should be addressed to the Conference Co-ordinator
Symposium enquiries specific should be addressed to:
Dennis Groth (Prof.)
Indiana University, USA
dgroth (@) indiana.edu
Georges Grinstein (Prof.)
University of Massachusetts Lowell,USA
grinstein (@) cs.uml.edu