Clustering and Multidimensional Scaling for Individual Difference Extraction

- This paper proposes methods to obtain difference among subjects by using the degree of reliability of each subject based on the results of fuzzy clustering and multidimensional scaling (MDS). In addition, new fuzzy clustering and MDS, including the weights of reliability scores, are proposed to classify subjects. When we observe data consisting of values of objects with respect to variables, and such data are observed over multiple subjects, capturing the difference among subjects is important in many fields. In this paper, the degree of reliability is obtained through the optimality of convex clustering. Based on this idea, it is shown that the same difference over the subjects can be obtained, regardless of the difference in obtained latent structures, which are the result of dynamic fuzzy clustering and the result of MDS by a numerical example. From this, we show the robustness of the proposed reliability concerning the variety of the obtained latent structures of data.