  Recently ensemble selection for consensus clustering has emerged as a
research problem in Machine Intelligence. Normally consensus clustering
algorithms take into account the entire ensemble of clustering, where there is
a tendency of generating a very large size ensemble before computing its
consensus. One can avoid considering the entire ensemble and can judiciously
select few partitions in the ensemble without compromising on the quality of
the consensus. This may result in an efficient consensus computation technique
and may save unnecessary computational overheads. The ensemble selection
problem addresses this issue of consensus clustering. In this paper, we propose
an efficient method of ensemble selection for a large ensemble. We prioritize
the partitions in the ensemble based on diversity and frequency. Our method
selects top K of the partitions in order of priority, where K is decided by the
user. We observe that considering jointly the diversity and frequency helps in
identifying few representative partitions whose consensus is qualitatively
better than the consensus of the entire ensemble. Experimental analysis on a
large number of datasets shows our method gives better results than earlier
ensemble selection methods.
