A Survey on K-means Based Consensus Clustering
Abstract
individual clustering solutions into a consensus one, which agrees as
much as possible with existing individual clustering solutions.
These individual clustering solutions may be heterogeneous in
nature as they are obtained by multiple runs of different clustering
algorithms or multiple runs of same algorithm with dynamic
variable settings on the same dataset. There are two import key
issues in designing methodology for consensus clustering problem to
work on heterogeneous partitions. One is availability of good
consensus function that fixes the consensus partition by verifying
utilities of available existing clustered partitions. Another is use of
the efficient suitable clustering methodology to fit into consensus
clustering like k-means algorithm. K-means based consensus
clustering (KCC) is one of the prominent solution studied in recent
years of research. Hence, our survey here tries to cover recent and
major advances in k-means and consensus clustering separately.
Different approaches used by researchers to improve the results of
both paradigms individually are promising. Picking up innovative
solutions from both k-means and consensus clustering can build
better integrated and sophisticated KCC frameworks. This survey
can give better future directions for improving clustering quality in
heterogeneous environments through the means of KCC.
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