%0 Journal Article
%1 fischer87
%A Fisher, Douglas H.
%D 1987
%J Machine Learning
%K clustering climbing incremental learning hill inference concept formation conceptual
%N 2
%P 139--172
%T Knowledge Acquisition Via Incremental Conceptual Clustering
%V 2
%X 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.