2011/07/18 by Patrice Boizumault, Boizumault, Patrice, Bruno Crémilleux +7
Computer Science · #Constraint Satisfaction and Optimization #Data Mining Algorithms and Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Semantic Web and Ontologies #cs.LG
paper · pdf · doi:10.48550/arxiv.1107.3407
12 pages
arxiv created 2011/07/18 · openalex publication_date 2011/07/18 · arxiv updated 2011/07/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.