Symbolic clustering of large datasets

We present an approach to cluster large datasets that integrates the Kohonen Self Organizing Maps (SOM) with a dynamic clustering algorithm of symbolic data (SCLUST). A preliminary data reduction using SOM algorithm is performed. As a result, the individual measurements are replaced by micro-clusters. These micro-clusters are then grouped in a few clusters which are modeled by symbolic objects. By computing the extension of these symbolic objects, symbolic clustering algorithm allows discovering the natural classes. An application on a real data set shows the usefulness of this methodology.