Clustering Ensembles Based on Multi-classifier Fusion
Clustering ensembles can combine multiple partitions generated by different clustering methods into a final superior clustering result. Compared to single clustering algorithm, it can provide better solutions in terms of robustness, novelty and stability. In this paper, we proposed a new method named CEMF, i.e.,Clustering Ensembles Based on Multiclassifier Fusion. We combine the clustering ensembles method and multi-classifier method to deal with the clustering consensus problem. CEMF generates multiple partitions and create subspaces which can be used to constructs the local optimum classifiers. CEMF makes use of the advantage of multi-classifiers to assist clustering ensembles in different subspaces of data set Experiments carried out on some public data sets show that CEMF is comparable or better than classical clustering algorithms and traditional clustering ensembles methods. Its an effective and feasible method.
clustering clustering ensembles multiple classifier consensus/unction classification
Yu Huang Dorothy Monekosso Hui Wang
School of Computing and Mathematics.University of Uister, Jordanstown,Co.Antrim,BT37 0QB.UK
国际会议
厦门
英文
393-397
2010-10-29（万方平台首次上网日期，不代表论文的发表时间）