The 21st International Conference on Discovery Science (DS 2018) 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-2018 will be co-located with ISMIS 2018 (http://cyprusconferences.org/ismis2018/), the 24th International Symposium on Methodologies for Intelligent Systems. 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. Selected papers will be invited for a submission to a special issue in Machine Learning journal.

We invite submissions of research papers addressing all aspects of discovery science. We particularly welcome contributions that discuss the application of data analysis, data mining and other support techniques for scientific discovery including, but not limited to, biomedical, astronomical and other physics domains. Applications to massive, heterogeneous, continuous or imprecise data sets are of particular interests.

Possible topics include, but are not limited to:

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 and analysis
Machine learning for high-performance computing, grid and cloud computing
Applications of the above techniques to natural or social sciences