対象間の非類似性を外的基準としたカテゴリカル属性の尺度構成法

抄録

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.