Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Conjunctive conceptual clustering: a methodology and experimentation (learning)
Conjunctive conceptual clustering: a methodology and experimentation (learning)
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 2
Learning hidden causes from empirical data
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
Learning intermediate concepts in constructing a hierarchical knowledge base
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
Approaches to conceptual clustering
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
Conceptual Clustering in Knowledge Organization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automated Construction of Classifications: Conceptual Clustering Versus Numerical Taxonomy
IEEE Transactions on Pattern Analysis and Machine Intelligence
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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.