Link-based pairwise similarity matrix approach for fuzzy c-means clustering ensemble

Cluster ensemble offers an effective approach for aggregating multiple clustering results in order to improve the overall clustering robustness and stability. It also helps improve accuracy by combing clustering results from component methods that utilise different parameters (e.g., number of clusters), avoiding the need for carefully pre-setting the values of such parameters in a single clustering process. Since founded, many topics regarding cluster ensemble have been proposed and promising results gained. These include the generation of ensemble members and consensus of ensemble members. In this paper, link-based consensus methods for the ensemble of fuzzy c-means are proposed. Different from traditional clustering techniques, the clusters which are generated by fuzzy c-means are fuzzy sets. The proposed methods therefore employ a fuzzy graph to represent the relationships between component clusters upon which to derive the final ensemble clustering results. Using various benchmark datasets, the proposed methods are tested against typical traditional methods. The experimental results demonstrate that the proposed fuzzy-link-based clustering ensemble approach generally outperforms the others in terms of accuracy.