Clustering ensemble by clustering selected weighted clusters

Due to the fact that no single clustering approach is capable of producing the optimal result for any given data, the notion of clustering ensembles has emerged, which attempts to extract a novel and robust consensus clustering from a given ensemble of base clusterings of the data. While forming the consensus, weights can be assigned to the base clusterings or their constituent clusters to prioritise those that accurately represent the underlying structure of the data. In this paper, we present a novel method of cluster selection from base clusterings and subsequently merging selected clusters into desired number of clusters in order to build a high-quality consensus clustering without gaining access to the internal distribution of data points. The method has been shown to work well with a wide range of data and to be better than many well-known clustering methods.