%0 Journal Article %1 h1987knowledge %A Fisher, Douglas H. %D 1987 %J Machine Learning %K COMMUNE classit clustering community coweb detection %N 2 %P 139--172 %T Knowledge Acquisition Via Incremental Conceptual Clustering %U http://dx.doi.org/10.1023/A:1022852608280 %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. ER -

@article{h1987knowledge, 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 -}, added-at = {2010-04-22T13:30:57.000+0200}, author = {Fisher, Douglas H.}, biburl = {https://www.bibsonomy.org/bibtex/20edbe48f91025efea4af0a1a62433e42/folke}, description = {SpringerLink - Journal Article}, interhash = {36208ac57cc67951de85bd99b8fb8647}, intrahash = {0edbe48f91025efea4af0a1a62433e42}, journal = {Machine Learning}, keywords = {COMMUNE classit clustering community coweb detection}, month = {#sep#}, number = 2, pages = {139--172}, timestamp = {2010-04-22T13:30:57.000+0200}, title = {Knowledge Acquisition Via Incremental Conceptual Clustering}, url = {http://dx.doi.org/10.1023/A:1022852608280}, volume = 2, year = 1987 }