Computing Skypattern Cubes using Relaxation

International audienceWe propose an effective method to compute the skypattern cubes thanks to a relaxation strategy in the pattern mining process. Our approach is based on the fact that each node of the cube can be approximated by the set of edge-skypatterns (a relaxed form of skypatterns) w.r.t. the whole set of measures M. Then we transform the problem into a skyline cube mining in |M| dimensions. The set of edge-skypatterns can be efficiently mined by using either a dynamic CSP method or an extended version of a static method based on the theoretical relationships between patterns and condensed representations of skypatterns. Experiments conducted on \dataset{UCI} datasets and on a real-life dataset (Mutagenicity) show the relevance and performance of our approach