  Due to its ability to combine multiple base clusterings into a probably
better and more robust clustering, the ensemble clustering technique has been
attracting increasing attention in recent years. Despite the significant
success, one limitation to most of the existing ensemble clustering methods is
that they generally treat all base clusterings equally regardless of their
reliability, which makes them vulnerable to low-quality base clusterings.
Although some efforts have been made to (globally) evaluate and weight the base
clusterings, yet these methods tend to view each base clustering as an
individual and neglect the local diversity of clusters inside the same base
clustering. It remains an open problem how to evaluate the reliability of
clusters and exploit the local diversity in the ensemble to enhance the
consensus performance, especially in the case when there is no access to data
features or specific assumptions on data distribution. To address this, in this
paper, we propose a novel ensemble clustering approach based on ensemble-driven
cluster uncertainty estimation and local weighting strategy. In particular, the
uncertainty of each cluster is estimated by considering the cluster labels in
the entire ensemble via an entropic criterion. A novel ensemble-driven cluster
validity measure is introduced, and a locally weighted co-association matrix is
presented to serve as a summary for the ensemble of diverse clusters. With the
local diversity in ensembles exploited, two novel consensus functions are
further proposed. Extensive experiments on a variety of real-world datasets
demonstrate the superiority of the proposed approach over the state-of-the-art.
