  Clustering ensemble, or consensus clustering, has emerged as a powerful tool
for improving both the robustness and the stability of results from individual
clustering methods. Weighted clustering ensemble arises naturally from
clustering ensemble. One of the arguments for weighted clustering ensemble is
that elements (clusterings or clusters) in a clustering ensemble are of
different quality, or that objects or features are of varying significance.
However, it is not possible to directly apply the weighting mechanisms from
classification (supervised) domain to clustering (unsupervised) domain, also
because clustering is inherently an ill-posed problem. This paper provides an
overview of weighted clustering ensemble by discussing different types of
weights, major approaches to determining weight values, and applications of
weighted clustering ensemble to complex data. The unifying framework presented
in this paper will help clustering practitioners select the most appropriate
weighting mechanisms for their own problems.
