A Flexible Iterative Framework for Consensus Clustering

5 Aug 2014 Shaina Race Carl Meyer

A novel framework for consensus clustering is presented which has the ability to determine both the number of clusters and a final solution using multiple algorithms. A consensus similarity matrix is formed from an ensemble using multiple algorithms and several values for k. A variety of dimension reduction techniques and clustering algorithms are considered for analysis... (read more)

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