Fisher, D. H.: Knowledge Acquisition Via Incremental Conceptual Clustering. In: Machine Learning 2 (1987), Nr. 2, S. 139-172
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.}
}
Fisher, D. H.: Knowledge Acquisition Via Incremental Conceptual Clustering. In: Machine Learning 2 (1987), Nr. 2, S. 139-172
[Volltext]
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.
-}
}