Mining Multi-Level Frequent Itemsets Under Constraints
Mining association rules is a task of data mining, which extracts knowledge in the form of significant implication relation of useful items (objects) from a database. Mining multilevel association rules uses concept hierarchies, also called taxonomies and defined as relations of type 'Is-a' between objects, to extract rules that items belong to different levels of abstraction. These rules are more useful, more refined and more interpretable by the user. Several algorithms have been proposed in the literature to discover the multilevel association rules. In this paper, the authors are interested in the problem of discovering multi-level frequent itemsets under constraints, involving the user in the research process. They proposed a technique for modeling and interpretation of constraints in a context of use of concept hierarchies.