Spectral clustering ensemble model based on distance decision. Issue 13 (1st June 2018)

• Record Type: Journal Article
• Authors: Fei, Bowen; Qiu, Yunfei; Liu, Daqian
• Is Part Of: Electronics letters. Volume 54:Issue 13(2018)
• Page Start: 823
• Page End: 825
• Publication Date: 2018-06-01
• Subjects: matrix algebra -- pattern clustering -- learning (artificial intelligence) -- fuzzy set theory; SC ensemble model -- distance decision -- cumulative distance matrix -- density peaks algorithm -- improved DP algorithm -- spectral clustering ensemble model -- joint Laplacian matrix -- fuzzy theory -- University of California Irvine machine learning database; Electronics -- Periodicals; 621.381
• DOI: 10.1049/el.2018.0424
• Languages: English
• ISSNs: 0013-5194

Abstract

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.