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DS 2010 : Discovery Science

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Conference Series : Discovery Science
 
Link: http://www.cse.unsw.edu.au/~achim/DS10/
 
When Oct 6, 2010 - Oct 8, 2010
Where Canberra, Australia
Abstract Registration Due May 5, 2010
Submission Deadline May 15, 2010
Notification Due Jun 17, 2010
Final Version Due Jul 5, 2010
Categories    discovery science   data mining   knowledge discovery
 

Call For Papers

DS-2010 provides an open forum for intensive discussions and exchange of new ideas among researchers working in the area of Discovery Science. The scope of the conference includes the development and analysis of methods for automatic scientific knowledge discovery, machine learning, intelligent data analysis, theory of learning, as well as their application to knowledge discovery. Especially welcome are papers that strongly focus on the discovery aspect of the reported work. The proceedings of DS-2010 will most likely appear in the Lecture Notes in Artificial Intelligence Series by Springer-Verlag.

We invite submissions of research papers addressing all aspects of discovery science. We particularly welcome contributions that discuss the application of scientific knowledge discovery and other support techniques including, but not limited to, biomedical, astronomical, space, chemistry, and physics domains.

Submission Topics

Possible topics include, but are not limited to:

* Logic and philosophy of scientific discovery
* Knowledge discovery, machine learning and statistical methods
* Ubiquitous Knowledge Discovery
* Data Streams, Evolving Data and Models
* Change Detection and Model Maintenance
* Active Knowledge Discovery
* Learning from Text and web mining
* Information extraction from scientific literature
* Knowledge discovery from heterogeneous, unstructured and multimedia data
* Knowledge discovery in network and link data
* Knowledge discovery in social networks
* Data and knowledge visualization
* Spatial/Temporal Data
* Mining graphs and structured data
* Planning to Learn
* Knowledge Transfer
* Computational Creativity
* Human-machine interaction for knowledge discovery and management
* Biomedical knowledge discovery, analysis of micro-array and gene deletion data
* Machine Learning for High-Performance Computing, Grid and Cloud Computing
* Applications of the above techniques to natural or social sciences
* Other applications of the above techniques

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