Abstract
When we use multidimensional scaling (MDS) to analyze a dataset in which the magnitude of the values considerably differ (vary) among objects, for example, customer purchase history data, it often occurs that the only points of objects with large values scatter well in the configuration, but the others with small values do not scatter well. It is said that joint uses of MDS and cluster analysis is often desirable. In this study, we showed that the joint use of MDS and cluster analysis also worked well in the above case, through the analysis of two-mode three-way proximity data such as several variance-covariance matrices of objects obtained from each condition. We also used an external analysis with MDS to show the information about each of the two modes on the same configuration simultaneously, in order to easily observe the global structure of data through the configuration.
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