Formal concept analysis and text mining

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Our research program addresses two main directions. Formal Concept Analysis (FCA) and text mining. Our first research direction is to pursue ongoing work in the field of FCA and applications, in particular data mining applications. FCA can be viewed as a hierarchical conceptual clustering approach that reveals the significant patterns of commonalties and relationships within tabular data based on the formal framework of Galois (or concept) lattices. Pioneered by Wille and myself in the early eighties, this field has largely expanded and it has become over the last ten years a well established discipline with an elaborated theoretical basis, a large array of applications (data mining, bioinformatics, software engineering, information retrieval, linguistics, psychology, ...) and software tools (commercial and open source), and a growing international community. In the next grant period, we plan to focus on data mining applications within the following main themes: multilevel association rule data mining condensed representation, complex data mining, approximate condensed representations and incremental frequent pattern mining for performance optimization in data warehouses.