This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Automated Construction of Classifications: Conceptual Clustering Versus Numerical Taxonomy
April 1983 (vol. 5 no. 4)
pp. 396-410
Ryszard S. Michalski, Department of Computer Science, University of Illinois, Urbana, IL 61801.
Robert E. Stepp, Department of Computer Science, University of Illinois, Urbana, IL 61801.
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.
Citation:
Ryszard S. Michalski, Robert E. Stepp, "Automated Construction of Classifications: Conceptual Clustering Versus Numerical Taxonomy," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 5, no. 4, pp. 396-410, April 1983, doi:10.1109/TPAMI.1983.4767409
Usage of this product signifies your acceptance of the Terms of Use.