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An Information Measure for Classification
Christopher S. Wallace (The University of Sydney), D. M. Boulton (The University of Sydney)
Résumé : This paper derives a measure of the goodness of a classification based on information theory. A classification is regarded as a method of economical statistical encoding of the available attribute information. The measure may be used to compare the relative goodness of classifications produced by different methods or as the basis of a classification procedure. A classification program, ‘SNOB’, has been written for the University of Sydney KDF 9 computer, and first tests show good agreement with conventional taxonomy.