19th International Conference on Discovery Science(DS 2016)
When : 2016-10-19
Where : Bari, Italy
Submission Deadline : 2016-05-21
Categories : Artificial Intelligence , Data Mining
19th International Conference on Discovery Science(DS 2016)
Oct 19, 2016 - Oct 21, 2016
Bari, Italy
Call For Papers
The 19th International Conference on Discovery Science (DS 2016) 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
discovering scientific knowledge, coming from machine learning, data mining, intelligent data analysis, big data analysis as well as their application in various scientific domains.
We welcome papers that focus on the analysis of different types of massive and complex data, including structured, spatio-temporal and network data. We particularly welcome papers addressing applications. Finally, we would like to encourage contributions from the areas of computational
scientific discovery, mining scientific data, computational creativity and discovery informatics.
DS-2016 will be co-located with ALT-2016 (http://www.comp.nus.edu.sg/~fstephan/alt/alt2016/), the 27th International Conference on Algorithmic Learning Theory. The two conferences will be held in parallel, and will share their invited talks.
Traditionally the proceedings of DS series appear in the Lecture Notes in Artificial Intelligence Series by Springer-Verlag. In addition a special issue on Discovery Science is planned in Machine Learning journal.
Topics
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