Descriptive concept extraction with exceptions by hybrid clustering

Natural concept modelling aims at representing numerically semantic knowledge; generally, experts are asked to provide examples of linguistic terms associated with numerical data descriptions. We propose to exploit directly non labelled databases to extract the concepts that enable a semantic description of the data. Our method consists in identifying the subgroups corresponding to the concepts and then representing them as fuzzy subsets. For the identification step, we propose an algorithm based on a conjugate iterative use of the single linkage hierarchical clustering algorithm and the fuzzy c-means, that explicitly takes into account both a separability objective and a compactness aim; the description step builds membership functions as generalized Gaussians. The adequacy of the results with spontaneous descriptions is illustrated on artificial and real databases.