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Découverte de motifs graduels partiellement ordonnés : application aux données d'expériences scientifiques
In EGC 2018, vol. RNTI-E-34, pp.227-238
The sequential data is today omnipresent and covers an important range of application domains.
Sequence pattern mining allows to extract information and knowledge that can be of high added value.
However, when the sequence data is rich in numerical data finer data mining methods are required to
be able to extract more expressive knowledge representing the variability of numerical values and their
possible interdependence. In this paper, we present a new method for the discovery of frequent gradual
sequences represented by directed graphs (DAG), from a data sources of sequences in RDF (Resource
Description Framework). The former ones, are first transformed into pog (partially ordered gradual
sequences) DAGs labeled at the arcs and the nodes. Then, on these graphs, we apply an algorithm which
mines and discovers the frequent pog subgraphs. Experiments on two real datasets in biology have shown
the feasibility and the relevance of our approach.