Therefore, a hierarchy of such CPO-patterns guides the interpretation task, helps experts in better understanding the extracted patterns, and minimises the chance of overlooking interesting CPO-patterns. Moreover, a generalisation order on items is also revealed, and multilevel CPO-patterns are obtained. The hierarchical RCA result allows to directly obtain and organise the relationships between the extracted CPO-patterns. To address these problems, we present and formalise an original approach within the framework of Relational Concept Analysis (RCA), referred to as RCA-SEQ, that focuses on facilitating the interpretation task of experts. In spite of the fewer number of obtained CPO-patterns, their analysis is still a challenging task for experts since they are unorganised and besides, do not provide a global view of the discovered regularities. To diminish this number and thus the difficulty of the interpretation task, methods that directly extract a more compact representation of sequential patterns, namely closed partially-ordered patterns (CPO-patterns), were introduced. Methods for analysing sequential data generally produce a huge number of sequential patterns that have then to be evaluated and interpreted by domain experts. This work is intended for data analysts, to provide them with an overview on the different strategies offered by RCA.

Our theoretical proposition is complemented by an experimental evaluation of the exploration space size, based on a real dataset upon watercourses. These operators are based on a set of quantifiers which are studied in this paper: we indeed focus on the comparison of scaling quantifiers and highlight a generality relation between them. In complex analyses, it relies on the successive choice of scaling operators which affects the size and the understandability of the results. It iterates on the building of interconnected concept lattices, so that each concept in a lattice might be the cause of generating other concepts in other lattices. However, RCA comes with specific complexity issues. The classification output by RCA is a family of lattices whose graphical representation facilitates the analysis by an expert. It can be used to discover knowledge patterns and implication rules in multi-relational datasets. This is achieved by iterating on Formal Concept Analysis (FCA).

Relational Concept Analysis (RCA) has been designed to classify sets of objects described by attributes and relations between these objects.

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