POC - Partially Ordered Clustering: an agglomerative algorithm
نویسندگان
چکیده
In the field of multicriteria decision aid, considerable attention has been paid to supervised classification problems where the purpose is to assign alternatives into predefined ordered classes. In these approaches, often referred to as sorting methods, it is usually assumed that categories are either known a priori or can be identified by the decision maker. On the other hand, when the objective is to identify groups (clusters) of alternatives sharing similar characteristics, the problem is known as a clustering problem, also called an unsupervised learning problem. Recently, some multicriteria clustering procedures have been proposed aiming to discover data structures with totally ordered categories from a multicriteria perspective. Here, we propose an agglomerative clustering method based on a valued outranking relation. We suggest a method for regrouping alternatives into partially ordered classes. The model is based on the quality of partition that reflects the percentage of pairs of alternatives that are compatible with a decision-maker’s preferences.
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