Knowledge Acquisition Via Incremental Conceptual Clustering
- Cite this article as:
- Fisher, D.H. Machine Learning (1987) 2: 139. doi:10.1023/A:1022852608280
- 2.1k Downloads
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.