Asymmetry for dynamic fuzzy clustering models

This paper presents a clustering model which analyzes asymmetric similarity data through several times. In general, proximity data (for example, mobility data, input-output data, perceptual confusion data, etc.) are observed in an asymmetric form. If such data are obtained over several times, then 3-way data are constructed. In this paper, we focus on the 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.