Machine Learning

, Volume 3, Issue 4, pp 343–372

Conceptual Clustering, Categorization, and Polymorphy

Authors

  • Stephen José Hanson
  • Malcolm Bauer
Article

DOI: 10.1023/A:1022697818275

Cite this article as:
Hanson, S.J. & Bauer, M. Machine Learning (1989) 3: 343. doi:10.1023/A:1022697818275

Abstract

In this paper we describe WITT, a computational model of categorization and conceptual clustering that has been motivated and guided by research on human categorization. Properties of categories to which humans are sensitive include best or prototypical members, relative contrasts between categories, and polymorphy (neither necessary nor sufficient feature rules). The system uses pairwise feature correlations to determine the “similarity” between objects and clusters of objects, allowing the system a flexible representation scheme that can model common-feature categories and polymorphous categories. This intercorrelation measure is cast in terms of an information-theoretic evaluation function that directs WITT'S search through the space of clusterings. This information-theoretic similarity metric also can be used to explain basic-level and typicality effects that occur in humans. WITT has been tested on both artificial domains and on data from the 1985 World Almanac, and we have examined the effect of various system parameters on the quality of the model's behavior.

Conceptual clusteringcategorizationcorrelationpolymorphyknowledge structurescoherence
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© Kluwer Academic Publishers 1989