Spectral clustering ensemble model based on distance decision. Issue 13 (1st June 2018) - Fei, Bowen; Qiu, Yunfei; Liu, Daqian
Abstract : Spectral clustering (SC) is a kind of clustering method which shows superior performance in recent years; however, the method is sensitive to the initial scale parameter. In order to improve the accuracy and stability of clustering, this Letter proposes a novel approach of SC ensemble model based on distance decision. First of all, the model performs several times clustering for data samples by SC, and corresponding joint Laplacian matrix is obtained. Then, a new method of distance decision is proposed, the joint Laplacian matrix is transformed into a cumulative distance matrix using the fuzzy theory. Finally, the distance matrix is introduced into the density peaks (DP) algorithm, and the final results of clustering are obtained by the improved DP algorithm for clustering ensemble. The experimental results show that the clustering ensemble model proposed in this Letter is more effective than other classical clustering ensemble model on the 12 data sets in University of California Irvine machine learning database.