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2016 | OriginalPaper | Buchkapitel

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

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

Erschienen in: Graph-Based Representation and Reasoning

Verlag: Springer International Publishing

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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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Vorheriges Kapitel Exploring the Usefulness of Formal Concept Analysis for Robust Detection of Spatio-temporal Spike Patterns in Massively Parallel Spike Trains

Nächstes Kapitel The Interpretation of Branching Time Diagrams

http://icube-sdc.unistra.fr/en/img_auth.php/c/c4/Mining_Hydroecological_Data_using_RCA.pdf.

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Titel: Extracting Hierarchies of Closed Partially-Ordered Patterns Using Relational Concept Analysis
verfasst von: Cristina Nica
Agnès Braud
Xavier Dolques
Marianne Huchard
Florence Le Ber
Verlag: Springer International Publishing
Buch: Graph-Based Representation and Reasoning
Print ISBN: 978-3-319-40984-9

Electronic ISBN: 978-3-319-40985-6

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-40985-6_2

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