Relational Concept Analysis: An approach for classifying and mining multi-relational data
Résumé
Galois lattices and concept lattices are core structures of a data analysis framework (Formal Concept Analysis, or FCA for short) for extracting an ordered set of concepts from a dataset, called a Formal Context, composed of objects described by attributes. This data analysis framework is currently applied to support various tasks, including information retrieval, data mining, building or maintaining class hierarchies in object-oriented software, software understanding or ontology alignment. Relational Concept Analysis (RCA) is an extension of the FCA framework to take into account multi-relational datasets, namely datasets composed of several categories of objects described by both attributes and inter-objects links. RCA generates a family of concept lattices, precisely one for each category of objects. The concepts in these lattices are connected via relational attributes that are abstractions of the initial links. This concept lattice family is a particular view on the dataset, which reveals implication rules as well as relevant connections between classified groups of objects. In this talk, we introduce RCA and we explain its strengths and limits. Then we give examples of some of its applications.
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