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.

@article{fischer87, author = {Fisher, Douglas H.}, title = {Knowledge Acquisition Via Incremental Conceptual Clustering}, journal = {Machine Learning}, year = {1987}, volume = {2}, number = {2}, pages = {139--172}, keywords = {inference, conceptual, concept, climbing, hill, clustering, formation, incremental, learning}, 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.} }

%0 = article %A = Fisher, Douglas H. %D = 1987 %T = Knowledge Acquisition Via Incremental Conceptual Clustering

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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@article{h1987knowledge, author = {Fisher, Douglas H.}, title = {Knowledge Acquisition Via Incremental Conceptual Clustering}, journal = {Machine Learning}, year = {1987}, volume = {2}, number = {2}, pages = {139--172}, url = {http://dx.doi.org/10.1023/A:1022852608280}, keywords = {detection, clustering, coweb, community, COMMUNE, classit}, 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. ER -} }

%0 = article %A = Fisher, Douglas H. %D = 1987 %T = Knowledge Acquisition Via Incremental Conceptual Clustering %U = http://dx.doi.org/10.1023/A:1022852608280