Asymmetry for dynamic fuzzy clustering models

This paper presents a clustering model which analizes asymmetric similarity data through several times. In general, proximity data, for example, mobility data, input-output data, perceptual confusion data, and so on? is observed as in the asymmetric form. If such data is obtained over several times, then 3-way data is constructed. In this paper, we will focus on clustering techniques for 3-way data and show that the proposed method can capture the properties of time difference and asymmetry between two objects in spite of the numbering of the clusters.