Conceptual Clustering, Learning from Examples, and Inference

Results obtained by COBWEB, a conceptual clustering system that organizes data so as to maximize inference abilities, are described, which generalizes the performance requirements typically associated with the better known task of learning from examples.

Abstract

Abstract Conceptual clustering has proved an effective means of summarizing data in an understandable manner. However, the recency of the conceptual clustering paradigm has allowed little exploration of conceptual clustering as a means of improving performance. This paper describes results obtained by COBWEB, a conceptual clustering system that organizes data so as to maximize inference abilities. The performance task for COBWEB (and implied for all conceptual clustering systems) generalizes the performance requirements typically associated with the better known task of learning from examples. Furthermore, criteria aimed at improving inference seem compatible with traditional conceptual clustering virtues of conceptual simplicity and comprehensibility.