Generation of a clustering ensemble based on a gravitational self-organising map
Clustering-ensemble methods have emerged recently as an effective approach to the problem of clustering, which is one of the fundamental data-analysis tools. Data clustering with an ensemble involves two steps: generation of the ensemble with single-clustering methods and the combination of the obtained solutions to produce a final consensus partition of the data. In this paper we first propose a novel clustering method, based on Kohonen's self-organising map and gravitational algorithm, and, second, investigate its performance in the generation of a clustering ensemble. The proposed method is able to discover clusters of complex shapes and determines the number of clusters automatically. Furthermore, its stochastic nature is beneficial in the construction of a diverse ensemble of partitions. Promising results of the presented method were obtained in comparison with three, relevant, single-clustering algorithms over artificial and real data sets.
self-organising map gravitational algorithm cluster analysis clustering-ensemble generation experimental comparison