A Multi-clustering Fusion Algorithm

Abstract

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 partition of the data which is not affected by initialization and overcomes the instabilities of clustering methods. Finally, the fusion procedure starts with the clusters produced by the combining part and finds the optimal number of clusters in the data set according to some predefined criteria. The unsupervised multi-clustering method implemented in this work is quite general. There is ample room for the implementation and testing with any existing clustering algorithm that has unstable results. Experiments using both simulated and real data sets indicate that the multi-clustering fusion algorithm is able to partition a set of data points to the optimal number of clusters not constrained to be hyper-spherically shaped.