Improving Inference through Conceptual Clustering

Book One

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 6

Machine Learning and Knowledge Acquisition

Conceptual clustering is an important way to summarize 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 intercorrelations 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.