Conceptual Clustering of Text Clusters

Common clustering techniques have the disadvantage that they do not provide intensional descriptions of the clusters obtained. Conceptual Clustering techniques, on the other hand, provide such descriptions, but are known to be rather slow. In this paper, we discuss a way of combining both techniques. We first cluster the documents by a variant of k–Means, using a thesaurus as background knowledge. This clustering reduces the large number of documents to a relatively small number of clusters, which can then be clustered conceptually in the second step.