Automated Construction of Classifications: Conceptual Clustering Versus Numerical Taxonomy

Authors:
Ryszard S. Michalski;Robert E. Stepp
Affiliations:
Department of Computer Science, University of Illinois, Urbana, IL 61801.;Department of Computer Science, University of Illinois, Urbana, IL 61801.
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
1983

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Abstract

A method for automated construction of classifications called conceptual clustering is described and compared to methods used in numerical taxonomy. This method arranges objects into classes representing certain descriptive concepts, rather than into classes defined solely by a similarity metric in some a priori defined attribute space. A specific form of the method is conjunctive conceptual clustering, in which descriptive concepts are conjunctive statements involving relations on selected object attributes and optimized according to an assumed global criterion of clustering quality. The method, implemented in program CLUSTER/2, is tested together with 18 numerical taxonomy methods on two exemplary problems: 1) a construction of a classification of popular microcomputers and 2) the reconstruction of a classification of selected plant disease categories. In both experiments, the majority of numerical taxonomy methods (14 out of 18) produced results which were difficult to interpret and seemed to be arbitrary. In contrast to this, the conceptual clustering method produced results that had a simple interpretation and corresponded well to solutions preferred by people.