Fisher, D. H. (1987),
'Knowledge Acquisition Via Incremental Conceptual Clustering', Machine Learning
2
(2)
, 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.
ER -
Fisher, D. H. (1987),
'Knowledge Acquisition Via Incremental Conceptual Clustering', Machine Learning
2
(2)
, 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.