Soft-Voting Clustering Ensemble
Clustering ensemble is a framework for combining multiple based clustering results of a set of objects without accessing the original feature of the objects. The majority voting method is widely used in clustering ensemble because of its simplicity, robustness and stability. In general, the existing voting methods only accept hard clustering results as input. In this paper we propose a new algorithm, Soft-Voting Clustering Ensemble (SVCE), which has better flexibility and generalization. The theory of SVCE is illustrated and the algorithm of SVCE is stated in detail firstly. Then 15 UCI datasets are used for the experiment and the results show that the proposed method has a better performance than state of the art ensemble methods in most cases, such as Majority Voting, Weighted Majority Voting, CSPA, MCLA, HGPA.