Anus̆ka Ferligoj

Direct multicriteria clustering algorithms

In a multicriteria clustering problem, optimization over more than one criterion is required. The problem can be treated in different ways: by reduction to a clustering problem with the single criterion obtained as a combination of the given criteria; by constrained clustering algorithms where a selected critetion is considered as the clustering criterion and all others determine the constraints; or by direct algorithms. In this paper two types of direct algorithms for solving multicriteria clustering problem are proposed: the modified relocation algorithm, and the modified agglomerative algorithm. Different elaborations of these two types of algorithms are discussed and compared. Finally, two applications of the proposed algorithms are presented.

Agglomerative Hierarchical Multicriteria Clustering Using Decision Rules

In a multicriteria clustering problem optimization over more than one criterion is required. The problem can be treated in different ways. In this paper the agglomerative hierarchical method based on decision rules for making decisions under uncertainty is proposed for solving multicriteria clustering problem. An application of proposed approach to Rosenberg and Kim kinship data is presented.