A Method for Scaling Categorical Attributes with Inter-Objec dissimilarity

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.