I want to write something about cluster analysis. I have started to read something about it and I faced with a concept which is called ''Aggregating''. What does it mean for cluster analysis? Do I have to use it? And I guess, researchers prefer to use hierarchical cluster analysis for aggregating. Generally, why do they prefer to use hierarchical cluster analysis for aggregating?
closed as off-topic by ttnphns, kjetil b halvorsen, mdewey, Peter Flom♦ Jul 28 '18 at 14:22
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According to Wikipedia:
Consensus clustering has emerged as an important elaboration of the classical clustering problem. Consensus clustering, also called aggregation of clustering (or partitions), refers to the situation in which a number of different (input) clusterings have been obtained for a particular dataset and it is desired to find a single (consensus) clustering which is a better fit in some sense than the existing clusterings.
This seems to imply that you "cluster your clusters," using multiple input clusters obtained from your dataset to find a better-fitting overarching cluster for your particular data. You essentially reconcile different clusters derived from different runs of your algorithm on the same data.
Topchy et al. Defined clustering aggregation as a maximum likelihood estimation problem, and they proposed an EM algorithm for finding the consensus clustering.