%0 
%0 Journal Article
%A Fisher, Douglas H.
%D 1987
%T Knowledge Acquisition Via Incremental Conceptual Clustering
%E 
%B Machine Learning
%C 
%I 
%V 2
%6
%N 2
%P 139--172
%& 
%Y 
%S 
%7
%8 September
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 
%3 article
%4 
%# 
%$ 
%F h1987knowledge
%K detection, clustering, coweb, community, COMMUNE, classit
%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.
ER  -
%Z 
%U http://dx.doi.org/10.1023/A:1022852608280
%+
%^

%0 
%0 Journal Article
%A Fisher, Douglas H.
%D 1987
%T Knowledge Acquisition Via Incremental Conceptual Clustering
%E 
%B Machine Learning
%C 
%I 
%V 2
%6
%N 2
%P 139--172
%& 
%Y 
%S 
%7
%8 September
%9
%? 
%! 
%Z 
%@ 
%(
%)
%*
%L
%M
%1 
%2 
%3 article
%4 
%# 
%$ 
%F fischer87
%K inference, conceptual, concept, climbing, hill, clustering, formation, incremental, learning
%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.
%Z 
%U 
%+
%^