Open AccessSpectral clustering ensemble model based on distance decision
Author(s) -
Fei Bowen,
Qiu Yunfei,
Liu Daqian
Publication year - 2018
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2018.0424
Subject(s) - cluster analysis , fuzzy clustering , correlation clustering , spectral clustering , cure data clustering algorithm , k medians clustering , data stream clustering , artificial intelligence , canopy clustering algorithm , pattern recognition (psychology) , computer science , mathematics , determining the number of clusters in a data set , ensemble learning , clustering high dimensional data , data mining
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.