Fisher, D. H.
(1987):
Knowledge Acquisition Via Incremental Conceptual Clustering.
In: Machine Learning,
Ausgabe/Number: 2,
Vol. 2,
Erscheinungsjahr/Year: 1987.
Seiten/Pages: 139-172.
[Kurzfassung] [BibTeX]
[Endnote]
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
Fisher, D. H.
(1987):
Knowledge Acquisition Via Incremental Conceptual Clustering.
In: Machine Learning,
Ausgabe/Number: 2,
Vol. 2,
Erscheinungsjahr/Year: 1987.
Seiten/Pages: 139-172.
[Volltext] [Kurzfassung] [BibTeX]
[Endnote]
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{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