RCA-Seq: An original approach for enhancing the analysis of sequential data based on hierarchies of multilevel closed partially-ordered patterns

Autor: Agnès Braud, Cristina Nica, Florence Le Ber

Publikováno v: Discrete Applied Mathematics
Discrete Applied Mathematics, 2020, 273, pp.232-251. ⟨10.1016/j.dam.2019.02.037⟩
Discrete Applied Mathematics, Elsevier, 2020, 273, pp.232-251. ⟨10.1016/j.dam.2019.02.037⟩

International audience; Methods for analysing sequential data generally produce a huge number of sequential patterns that have then to be evaluated and interpreted by domain experts. To diminish this number and thus the difficulty of the interpretati

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::84c77889736ec6cd453add808f6c8abf
https://doi.org/10.1016/j.dam.2019.02.037

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Exploration de données séquentielles à l’aide de l’analyse relationnelle de concepts

Autor: Nica, Cristina

Publikováno v: Artificial Intelligence [cs.AI]. Université de Strasbourg, 2017. English. ⟨NNT : 2017STRAD032⟩

Many sequential pattern mining methods have been proposed to discover useful patterns that describe the analysed sequential data. Several of these works have focused on efficiently enumerating all closed partially-ordered patterns (cpo-patterns), tha

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::def2ddd5b6272a861827d6aae3d2e475
https://tel.archives-ouvertes.fr/tel-01712510/document