This workshop welcomes a broad range of submissions addressing aforementioned problems. We are interested in 1) theoretical advances as well as algorithm developments in large scale visual analytics, 2) reports of practical applications and system innovations, and 3) large scale data sets as test bed for new developments, preferably with implemented standard benchmarks. The following list contains potentially interested topics (but not limited to):

- Large scale optimisation methods
- Adaption of standard algorithms to large scale settings
- Subspace learning for data representation
- Probabilistic models
- Tensoral data analysis
- Online learning for large scale problems
- Parallel / cloud computation based data mining
- Feature extraction for image / video analysis
- Relational data model
- Causal learning for visual data mining
- Clustering methods in large scale and dynamic settings

Submissions

Papers submitted to this workshop must not have been accepted for publication elsewhere. All papers must be formatted to IEEE Computer Society proceedings manuscript style. For detailed formatting instructions, please follow the IEEE ICDM formatting guidelines. Manuscripts should be limited to 6 double-colum pages. For submit your contribution, please proceed to the submission page.

Workshop Proceedings

Accepted papers will be included in the IEEE ICDM 2011 Workshops Proceedings volume published by IEEE Computer Society Press (also included in the IEEE Xplore Digital Library).