Kodo Keiryogaku (The Japanese Journal of Behaviormetrics)
Online ISSN : 1880-4705
Print ISSN : 0385-5481
ISSN-L : 0385-5481
A Method for Scaling Categorical Attributes with Inter-Objec dissimilarity
Kohei ADACHIHiroshi TANAKA
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1995 Volume 22 Issue 2 Pages 110-125

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Abstract

We propose a least square procedure called ASCLUD(Attribute Scaling using CLUster-Distance)which scales categorical attributes with profiles of objects on the attributes and dissimilarities between the objects. ASCLUD assumes that in a multidimensional space an object is represented as a cluster of the points which correspond to the attributes possessed by the object. Distances between the clusters are fitted to the inter-object dissimilarities using the generalized majorization algorithm. Examples are given to illustrate the use of ASCLUD and to compare ASCLUD with a previous dissimilarity model considering the weights of attributes. Some properties of ASCLUD are discussed.

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© The Behaviormetric Society of Japan
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