K-means-based consensus clustering: algorithms, theory and applications.
Consensus clustering aims to find a single partition which agrees as much as possible with existing basic partitions, which emerges as a promising solution to find cluster structures from heterogeneous data. It has been widely recognized that consensus clustering is effective to generate robust clus...
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| Online Access: | http://hdl.handle.net/2047/D20291355 |