Improving inference through conceptual clustering

COBWEB is presented, a conceptual clustering system that organizes data to maximize inference abilities by capturing attribute inter-correlations at classification tree nodes and generating inferences as a by-product of classification.

Abstract

Conceptual clustering is an important way to sununarize data in an understandable manner. However, the recency of the conceptual clustering paradigm has allowed little exploration of conceptual clustering as a means of improving performance. This paper presents COBWEB, a conceptual clustering system that organizes data to maximize inference abilities. It does this by capturing attribute inter-correlations at classification tree nodes and generating inferences as a by-product of classification. Results from the domains of soybean and thyroid disease diagnosis support the success of this approach.