Extracting Hierarchies of Closed Partially-Ordered Patterns Using Relational Concept Analysis

Cristina Nica (1) , Agnès Braud (1) , Xavier Dolques (1) , Marianne Huchard (2) , Florence Le Ber (1)

1 ICube - Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie
2 MAREL - Models And Reuse Engineering, Languages

This paper presents a theoretical framework for exploring temporal data, using Relational Concept Analysis (RCA), in order to extract frequent sequential patterns that can be interpreted by domain experts. Our proposal is to transpose sequences within relational contexts , on which RCA can be applied. To help result analysis, we build closed partially-ordered patterns (cpo-patterns), that are synthetic and easy to read for experts. Each cpo-pattern is associated to a concept extent which is a set of temporal objects. Moreover, RCA allows to build hierarchies of cpo-patterns with two generalisation levels, regarding the structure of cpo-patterns and the items. The benefits of our approach are discussed with respect to pattern structures.

Domaines: Génie logiciel [cs.SE]

Cristina Nica, Agnès Braud, Xavier Dolques, Marianne Huchard, Florence Le Ber. Extracting Hierarchies of Closed Partially-Ordered Patterns Using Relational Concept Analysis. ICCS 2016 - 22nd International Conference on Conceptual Structures, Jul 2016, Annecy, France. pp.17-30, ⟨10.1007/978-3-319-40985-6_2⟩. ⟨hal-01380407⟩