Machine Learning

, Volume 2, Issue 2, pp 139–172

Knowledge Acquisition Via Incremental Conceptual Clustering

  • Authors
  • Douglas H. Fisher
Article

DOI: 10.1023/A:1022852608280

Cite this article as:
Fisher, D.H. Machine Learning (1987) 2: 139. doi:10.1023/A:1022852608280

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.

Conceptual clusteringconcept formationincremental learninginferencehill climbing
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© Kluwer Academic Publishers 1987