Semi-supervised clustering with active constraint selection

Abstract

The paper presents the new approach to the semi-supervised fuzzy clustering, which is based on the extended optimization function and the proposed procedure to the active constraints selection. The approach is tested on the artificial and real data sets. Clustering results, obtained by proposed approach, are more accurate relative to the ground truth due to utilization of the additional information about class labels and its active selection.