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

Cristina Nica,Marianne Huchard,Agnès Braud,Xavier Dolques,Florence Le Ber

doi:10.1007/978-3-319-40985-6_2

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

Cristina Nica, Marianne Huchard + Show 3 more

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https://doi.org/10.1007/978-3-319-40985-6_2

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Publication Date: Jan 1, 2016
Citations: 8	License type: other-oa

Affiliation: French National Centre for Scientific Research, Montpellier Laboratory of Informatics, Robotics and Microelectronics, University of Montpellier, University of Strasbourg, Ecole Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)

#Relational Concept Analysis #Relational Contexts + Show 8 more

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Abstract

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.

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