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.
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Nica, C., Braud, A., Dolques, X., Huchard, M., Le Ber, F. (2016). Extracting Hierarchies of Closed Partially-Ordered Patterns Using Relational Concept Analysis. In: Haemmerlé, O., Stapleton, G., Faron Zucker, C. (eds) Graph-Based Representation and Reasoning. ICCS 2016. Lecture Notes in Computer Science(), vol 9717. Springer, Cham. https://doi.org/10.1007/978-3-319-40985-6_2
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DOI: https://doi.org/10.1007/978-3-319-40985-6_2
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