Knowledge Acquisition Via Incremental Conceptual Clustering.
Machine Learning, 2(2):139-172, 1987.
Douglas H. Fisher.
[doi]
[abstract]
[BibTeX]
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 -
Knowledge Acquisition Via Incremental Conceptual Clustering.
Machine Learning, 2(2):139-172, 1987.
Douglas H. Fisher.
[abstract]
[BibTeX]
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.