Iterated Consensus Clustering: A Technique We Can All Agree On

Cluster Analysis is a field of Data Mining used to extract underlying patterns in unclassified data. Many existing clustering algorithms are inadequate in that they require knowledge of how many clusters exist in the data, otherwise known as k, and that their underlying assumptions make them ineffective in certain situations. The method of Consensus Clustering seeks to rectify the latter problem by incorporating the results of multiple clustering algorithms to achieve one final grouping. We investigate a novel method of Iterative Consensus Clustering (ICC) which solves both issues. The iteration of the consensus clustering technique widens the eigengap associated with the Perron cluster, giving a more definitive, and more accurate, estimation of the number of clusters, k.