@article{fischer87, 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.}, author = {Fisher, Douglas H.}, interhash = {36208ac57cc67951de85bd99b8fb8647}, intrahash = {0edbe48f91025efea4af0a1a62433e42}, journal = {Machine Learning}, month = {September}, number = 2, pages = {139--172}, title = {Knowledge Acquisition Via Incremental Conceptual Clustering}, volume = 2, year = 1987 }