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

  • Cristina Nica
  • Agnès BraudEmail author
  • Xavier Dolques
  • Marianne Huchard
  • Florence Le Ber
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9717)

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.

Keywords

Relational Attribute Medical Examination Sequential Pattern Pattern Structure Concept Lattice 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Cristina Nica
    • 1
  • Agnès Braud
    • 1
    Email author
  • Xavier Dolques
    • 1
  • Marianne Huchard
    • 2
  • Florence Le Ber
    • 1
  1. 1.ICube, University of Strasbourg, CNRS, ENGEESStrasbourgFrance
  2. 2.LIRMM, University of Montpellier, CNRSMontpellierFrance

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