%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.