An improved weighted ensemble clustering based on two-tier uncertainty measurement

Abstract

Existing locally weighted ensemble clustering algorithms strive to weight each cluster and take into account the differences among all clusters, but they tend to ignore the basic cluster labels. The purpose of this paper is to combine the influence of cluster level and the base clustering level in a unified ensemble clustering framework. A novel two-level weighted ensemble cluster method (TWEC) is proposed, which inserts a global weighting strategy into a local ensemble cluster learning framework. First, the cluster uncertainty based on an entropy criterion is refined by considering the base clustering labels for each cluster. Then, the two-level uncertainty is converted to cluster reliability via improved ensemble-driven cluster validity measure. Finally, two novel consensus functions are developed. Experiments validate the effectiveness of the proposed TWEC framework by comparing it with ten comparison algorithms on fourteen real-world datasets and twenty synthetic datasets. The results show that TWEC framework can improve the robustness and stability of clustering.