Abstract
Clustering is described as a multistep process in which some of the steps are performed by a data analyst and some by a computer program. At present, those performed by a computer program do not produce any description of the generated clusters. The recently introduced method of conjunctive conceptual clustering overcomes this problem by requiring that each cluster has a conjunctive description built from relations on object attributes and closely “fitting” the cluster. The paper explains the above clustering method in terms of dynamic clustering and shows by an example its advantages over methods of numerical taxonomy from the viewpoint of cluster interpretation.
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