Conceptual clustering and its relation to numerical taxonomy
Artificial intelligence and statistics
Models of incremental concept formation
Artificial Intelligence
Concept formation in structured domains
Concept formation knowledge and experience in unsupervised learning
Theory-guided concept formation
Concept formation knowledge and experience in unsupervised learning
Conceptual Clustering, Categorization, and Polymorphy
Machine Learning
Machine Learning
Experiments with Incremental Concept Formation: UNIMEM
Machine Learning
Paper: Medical decision making based on inductive learning method
Artificial Intelligence in Medicine
Artificial Intelligence in Medicine
Structure discovery in medical databases: a conceptual clustering approach
Artificial Intelligence in Medicine
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The main interest of this research is to discover clinical implications from a large PTCA (Percutaneous Transluminal Coronary Angioplasty) database. A case-based concept formation model D-UNIMEM, a modified version of Lebowitz's UNIMEM, is proposed for this purpose. In this model, we integrated two kinds of class memberships: the feature-disjunction class membership and the index-conjunction class membership. The former is a polythetic clustering approach and serves at the early stage of concept formation. The latter allows only relevant instances to be placed in the same cluster and serves as the later stage of concept formation. D-UNIMEM could extract interesting correlations among features from the learned concept hierarchy.