Cluster Ensemble Based on Spectral Clustering
基于谱聚类的聚类集成算法

Spectral clustering has become increasingly popular in recent years. It can deal with arbitrary distribution dataset, however, it is sensitive to the scaling parameter. Cluster ensemble based on spectral clustering is proposed which utilizes the good robustness and generalization ability of cluster ensemble. Multiform clustering components are generated by exploiting the property of spectral clustering, and the connected triple algorithm which can expand the similarity information among data is used to compute the affinity matrix, then the affinity matrix is used by spectral clustering algorithm to produce ensemble results. In order to make the algorithm extensible to large scale applications, only the similarity among the rand sampling data and the similarity between the random sampling data and the rest data are computed by adopting the Nystr m sampling method. The proposed algorithm makes full use of the excellent performance of spectral clustering as well as avoids the selection of the accurate parameter in spectral clustering. Experiments show that compared with other common cluster ensemble techniques, the proposed algorithm is more excellent and efficient, and that it can provide a good way to solve data clustering and image segmentation problem.