Discovering Knowledge using a Constraint-based Language

Discovering pattern sets or global patterns is an attractive issue from the pattern mining community in order to provide useful information. By combining local patterns satisfying a joint meaning, this approach produces patterns of higher level and thus more useful for the data analyst than the usual local patterns, while reducing the number of patterns. In parallel, recent works investigating relationships between data mining and constraint programming (CP) show that the CP paradigm is a nice framework to model and mine such patterns in a declarative and generic way. We present a constraint-based language which enables us to define queries addressing patterns sets and global patterns. The usefulness of such a declarative approach is highlighted by several examples coming from the clustering based on associations. This language has been implemented in the CP framework.Comment: 12 page

Dagstuhl Annual Report January - December 2011

The International Conference and Research Center for Computer Science is a non-profit organization. Its objective is to promote world-class research in computer science and to host research seminars which enable new ideas to be showcased, problems to be discussed and the course to be set for future development in this field. The work being done to run this informatics center is documented in this report for the business year 2011

Constraint programming meets machine learning and data mining (Dagstuhl Seminar 11201)

This report documents the programme and the outcomes of Dagstuhl Seminar 11201 "Constraint Programming meets Machine Learning and Data Mining". Our starting point in this seminar was that machine learning and data mining have developed largely independently from constraint programming till now, but that it is increasingly becoming clear that there are many opportunities for interactions between these areas: on the one hand, data mining and machine learning can be used to improve constraint solving; on the other hand, constraint solving can be used in data mining in machine learning. This seminar brought together prominent researchers from both communities to discuss these opportunities