Dynamic clustering model for ordinal similarity

This paper proposes a clustering model for ordinal similarity data. The data is 3-way data, which is observed by similarities of objects for several times. The essential merit of this model is to capture the differences of clusterings while keeping the feature of object ordering. In order to keep this feature, the monotone relation is used for fitting the data and the model. The fitness is calculated based on the monotone regression principle (Kruskal, 1964)