Clustering ensemble is an important branch of clustering,which is used to fuse multiple base clusters to generate robust and high-quality final clustering partitions. At present, many researchers focus on the clustering ensemble method of transforming the original information into a co - association matrix to obtain the final clustering partition through?the co - association matrix. However, mostresearchers ignore that the clustering results are easily affected by noise,and the time and space complexity of the co-association matrix ishigh when the amount of data is large. In order to solve the above problems,we design a clustering ensemble method based on similaritybetween clusters ( CSCE) .?
The method firstly finds the similarity between the original objects based on the evidence accumulationmodel,and divides the original objects into several small clusters. Then a new similarity calculation method is used to calculate thesimilarity between clusters and form the similarity matrix between clusters. Finally,the cluster similarity matrix is divided into the finalclustering results by the method of normalized cut ( NCUT) . The proposed method combines low quality abnormal objects into similarclusters according to similarity, and experiments are conducted on 8 datasets. It is showed that the proposed method not only has a goodclustering effect,but also solves the problem of high time and space complexity of traditional co-association matrix.