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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Fisher, D.H. Knowledge acquisition via incremental conceptual clustering. Mach Learn 2, 139–172 (1987). https://doi.org/10.1007/BF00114265
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DOI: https://doi.org/10.1007/BF00114265