No abstract

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.

Relational Concept Analysis (RCA) has been designed to classify sets of objects described by attributes and relations between these objects. This is achieved by iterating on Formal Concept Analysis (FCA). It can be used to discover knowledge patterns and implication rules in multi-relational datasets. The classification output by RCA is a family of lattices whose graphical representation facilitates the analysis by an expert. However, RCA comes with specific complexity issues. 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. In complex analyses, it relies on the successive choice of scaling operators which affects the size and the understandability of the results. 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. Our theoretical proposition is complemented by an experimental evaluation of the exploration space size, based on a real dataset upon watercourses. This work is intended for data analysts, to provide them with an overview on the different strategies offered by RCA.

International audienceEconomic development based on industrialization, intensive agriculture expansion and population growth places greater pressure on water resources through increased water abstraction and water quality degradation [40]. River pollution is now a visible issue, with emblematic ecological disasters following industrial accidents such as the pollution of the Rhine river in 1986 [31]. River water quality is a pivotal public health and environmental issue that has prompted governments to plan initiatives for preserving or restoring aquatic ecosystems and water resources [56]. Water managers require operational tools to help interpret the complex range of information available on river water quality functioning. Tools based on statistical approaches often fail to resolve some tasks due to the sparse nature of the data. Here we describe HydroQual, a tool to facilitate visual analysis of river water quality. This tool combines spatiotem-poral data mining and visualization techniques to perform tasks defined by water experts. We illustrate the approach with a case study that illustrates how the tool helps experts analyze water quality. We also perform a qualitative evaluation with these experts