Skypattern mining: From pattern condensed representations to dynamic constraint satisfaction problems

Willy Ugarte Rojas 1 Patrice Boizumault 1 Bruno Crémilleux 1 Alban Lepailleur 2 Samir Loudni 1 Marc Plantevit 3 Chedy Raïssi 4 Arnaud Soulet 5
1 Equipe CODAG - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
3 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
4 ORPAILLEUR - Knowledge representation, reasonning
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
5 BDTLN - Bases de données et traitement des langues naturelles
LI - Laboratoire d'Informatique de l'Université de Tours
Abstract : Data mining is the study of how to extract information from data and express it as useful knowledge. One of its most important subfields, pattern mining, involves searching and enumerating interesting patterns in data. Various aspects of pattern mining are studied in the theory of computation and statistics. In the last decade, the pattern mining community has witnessed a sharp shift from efficiency-based approaches to methods which can extract more meaningful patterns. Recently, new methods adapting results from studies of economic efficiency and multi-criteria decision analyses such as Pareto efficiency, or skylines, have been studied. Within pattern mining, this novel line of research allows the easy expression of preferences according to a dominance relation. This approach is useful from a user-preference point of view and tends to promote the use of pattern mining algorithms for non-experts. We present a significant extension of our previous work [1] and [2] on the discovery of skyline patterns (or “skypatterns”) based on the theoretical relationships with condensed representations of patterns. We show how these relationships facilitate the computation of skypatterns and we exploit them to propose a flexible and efficient approach to mine skypatterns using a dynamic constraint satisfaction problems (CSP) framework. We present a unified methodology of our different approaches towards the same goal. This work is supported by an extensive experimental study allowing us to illustrate the strengths and weaknesses of each approach.
Type de document :
Article dans une revue
Artificial Intelligence, Elsevier, 2017, 244, pp. 48-69. <10.1016/j.artint.2015.04.003>
Liste complète des métadonnées
Contributeur : Chedy Raïssi <>
Soumis le : lundi 31 août 2015 - 17:27:55
Dernière modification le : jeudi 15 juin 2017 - 09:09:41



Willy Ugarte Rojas, Patrice Boizumault, Bruno Crémilleux, Alban Lepailleur, Samir Loudni, et al.. Skypattern mining: From pattern condensed representations to dynamic constraint satisfaction problems. Artificial Intelligence, Elsevier, 2017, 244, pp. 48-69. <10.1016/j.artint.2015.04.003>. <hal-01188928>



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