A classical problem of Pattern Recognition consists in looking for an operator of classification (a ‘classifier’) induced from a learning set on which classes are known. A problem frequently encountered in practice is that of looking for an operator of clustering (a ‘clusterfier’, in opposition to ‘classifier’) from a learning set on which clusters are also known. In the first case, we have to find an operator which allocates each new object to one of the classes defined by the learning set. In the second case, we have to find an operator which detects classes in the complete population, taking in account as well as possible the information given by the classes on the learning set. We propose a new approach permitting to induce and aggregation index from knowledge acquiring on the learning set; the aggregation index thus obtained permits to induce a hierarchy which infers the desired classes on the whole population.

A nearest neighbours algorith with validity constraints has been realized to induce the final hierarchy. We obtain a CPU time clearly shorter than with the classical hierarchical ascending classification algorithm which does not use inference.

This program has permitted to find aggregation indices adapted to particular learning sets (elongated classes, spherical class with central kernel, half spherical class with central kernel, noising elongated classes…), and some of new indices permit to recognize more specific classes than the usual indices.