Cluster Ensemble and Multi-Objective Clustering Methods
Katti Faceli (Federal University of São Carlos, Brazil), Andre C.P.L.F. de Carvalho (University of São Paulo, Brazil) and Marcilio C.P. de Souto (Federal University of Rio Grande do Norte, Brazil)
Copyright: © 2008
Clustering is an important tool for data exploration. Several clustering algorithms exist, and new algorithms are frequently proposed in the literature. These algorithms have been very successful in a large number of real-world problems. However, there is no clustering algorithm, optimizing only a single criterion, able to reveal all types of structures (homogeneous or heterogeneous) present in a dataset. In order to deal with this problem, several multi-objective clustering and cluster ensemble methods have been proposed in the literature, including our multi-objective clustering ensemble algorithm. In this chapter, we present an overview of these methods, which, to a great extent, are based on the combination of various aspects of traditional clustering algorithms.