Knowledge Acquisition Via Incremental Conceptual Clustering |
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Authors: | Fisher Douglas H |
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Affiliation: | (1) Irvine Computational Intelligence Project, Department of Information and Computer Science, University of California, 92717 Irvine, California, U.S.A. |
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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. |
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Keywords: | Conceptual clustering concept formation incremental learning inference hill climbing |
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