Exploring Temporal Data Using Relational Concept Analysis: An Application to Hydroecology

Abstract : This paper presents an approach for mining temporal data, based on Relational Concept Analysis (RCA), that has been developed for a real world application. Our data are sequential samples of biological and physico-chemical parameters taken from watercourses. Our aim is to reveal meaningful relations between the two types of parameters. To this end, we propose a comprehensive temporal data mining process starting by using RCA on an ad hoc temporal data model. The results of RCA are converted into closed partially ordered patterns to provide experts with a synthetic representation of the information contained in the lattice family. Patterns can also be filtered with various measures, exploiting the notion of temporal objects. The process is assessed through some quantitative statistics and qualitative interpretations resulting from experiments carried out on hydroecological datasets.
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Marianne Huchard; Sergei Kuznetsov. CLA: Concept Lattices and their Applications, Jul 2016, Moscow, Russia. 13th International Conference on Concept Lattices and their Applications, 1624, pp.299-311, 2016, CEUR Workshop Proceedings. 〈https://cla2016.hse.ru/〉
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Cristina Nica, Agnès Braud, Xavier Dolques, Marianne Huchard, Florence Le Ber. Exploring Temporal Data Using Relational Concept Analysis: An Application to Hydroecology. Marianne Huchard; Sergei Kuznetsov. CLA: Concept Lattices and their Applications, Jul 2016, Moscow, Russia. 13th International Conference on Concept Lattices and their Applications, 1624, pp.299-311, 2016, CEUR Workshop Proceedings. 〈https://cla2016.hse.ru/〉. 〈hal-01380404〉

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