An application of AI techniques to structuring objects into an optimal conceptual hierarchy

Authors:
Ryatard S. Michalski;Robert E. Stepp
Affiliations:
Department of Computer Science, University of Illinois at Urbana-Champalgn;Department of Computer Science, University of Illinois at Urbana-Champalgn
Venue:
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 1
Year:
1981

Citing 1
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Abstract

A method of "learning from observation" is presented which structures a collection of objects into hierarchies of subcategories, such that each subcategory la characterised by a conjunctive description involving relations on selected object attributes. The conjunctive descriptions supporting from each node are mutually disjoint and optimal aa a group according to a flexibly defined criterion. Each level of the hierarchy is determined by an iterative procaaa which respectively applies a veralon of the A* search algorithm. Experiments with the program CLUSTER/PAF implementing the method indicate that the obtained hierarchies represent solutions which have a simple conceptual Interpretation and which seem to agree well with the way people structure objects.