Handling multicriteria preferences in cluster analysis

In the framework of multicriteria decision aid, a lot of interest has been devoted to sorting problems, in which the set of categories is pre-defined. Besides, preference oriented multicriteria clustering has received little attention. Usual geometric and related metrics are not well suited for this problem. Here, we propose a clustering method based on a valued indifference relation inspired by outranking methods. We suggest a method (based on comparing cluster centers and an average net flow score of clusters) to build a complete ranking of the set of clusters, that is, a way of defining a set of ordered categories for sorting purposes. The new approach performs very well in some examples.