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

Combining CSP and Constraint-based Mining for Pattern Discovery

International audienceA well-known limitation of a lot of data mining methods is the huge number of patterns which are discovered: these large outputs hamper the individual and global analysis performed by the end-users of data. That is why discovering patterns of higher level is an active research field. In this paper, we investigate the relationship between local constraint-based mining and constraint satisfaction problems and we propose an approach to model and mine patterns combining several local patterns, i.e., patterns defined by n-ary constraints. The user specifies a set of n-ary constraints and a constraint solver generates the whole set of solutions. Our approach takes benefit from the recent progress on mining local patterns by pushing with a solver on local patterns all local constraints which can be inferred from the n-ary ones. This approach enables us to model in a flexible way any set of constraints combining several local patterns. Experiments show the feasibility of our approach

Combining CSP and Constraint-based Mining for Pattern Discovery

International audienceA well-known limitation of a lot of data mining methods is the huge number of patterns which are discovered: these large outputs hamper the individual and global analysis performed by the end-users of data. That is why discovering patterns of higher level is an active research field. In this paper, we investigate the relationship between local constraint-based mining and constraint satisfaction problems and we propose an approach to model and mine patterns combining several local patterns, i.e., patterns defined by n-ary constraints. The user specifies a set of n-ary constraints and a constraint solver generates the whole set of solutions. Our approach takes benefit from the recent progress on mining local patterns by pushing with a solver on local patterns all local constraints which can be inferred from the n-ary ones. This approach enables us to model in a flexible way any set of constraints combining several local patterns. Experiments show the feasibility of our approac

Advances in knowledge discovery and data mining Part II

19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II</p