Knowledge Acquisition Via Incremental Conceptual Clustering

Authors:
Douglas H. Fisher
Affiliations:
Irvine Computational Intelligence Project, Department of Information and Computer Science, University of California, Irvine, California 92717, U.S.A. DFISHER@ICS.UCI.EDU
Venue:
Machine Learning
Year:
1987

Citing 0
Cited 375

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Abstract

Conceptual clustering is an important way of summarizing and explaining data. However, the recent formulation of this paradigm has allowed little exploration of conceptual clustering as a means of improving performance. Furthermore, previous work in conceptual clustering has not explicitly dealt with constraints imposed by real world environments. This article presents COBWEB, a conceptual clustering system that organizes data so as to maximize inference ability. Additionally, COBWEB is incremental and computationally economical, and thus can be flexibly applied in a variety of domains.