A MULTI-CLUSTERING FUSION SCHEME FOR DATA PARTITIONING
Dimitrios Frossyniotis, Christos Pateritsas, Andreas Stafylopatis · International Journal of Neural Systems · 2005
A multi-clustering fusion method is presented based on combining several runs of a clustering algorithm resulting in a common partition. More specifically, the results of several independent runs of the same clustering algorithm are appropriately combined to obtain a distinct partition of the data which is not affected by initialization and overcomes the instabilities of clustering methods. Subsequently, a fusion procedure is applied to the clusters generated during the previous phase to determine the optimal number of clusters in the data set according to some predefined criteria.