Machine Learning, Clustering, and Polymorphy
This addresses the challenge of improving categorization models for applications in expert systems and information retrieval, though it appears incremental as it builds on existing clustering schemes.
The paper tackles the problem of modeling human categorization by developing a machine induction program (WITT) that captures properties like prototypical members and polymorphy, showing it to be more consistent with human categorization than traditional AI methods.
This paper describes a machine induction program (WITT) that attempts to model human categorization. Properties of categories to which human subjects are sensitive includes best or prototypical members, relative contrasts between putative categories, and polymorphy (neither necessary or sufficient features). This approach represents an alternative to usual Artificial Intelligence approaches to generalization and conceptual clustering which tend to focus on necessary and sufficient feature rules, equivalence classes, and simple search and match schemes. WITT is shown to be more consistent with human categorization while potentially including results produced by more traditional clustering schemes. Applications of this approach in the domains of expert systems and information retrieval are also discussed.