DocumentCode
2666106
Title
A self-adaptive spectral clustering algorithm
Author
Xiaoyan, Cai ; Guanzhong, Dai ; Libin, Yang ; Guoqing, Zhang
Author_Institution
Sch. of Autom. Control, Northwestern Polytech. Univ., Xi´´an
fYear
2008
fDate
16-18 July 2008
Firstpage
551
Lastpage
553
Abstract
Most existing algorithms on spectral clustering are not able to determine the number of clusters. In this paper, we prove theoretically that the eigenvectors of the affinity matrix can be used directly to cluster the data points. And we suggest exploiting the structure of the eigenvectors to infer automatically the number of clusters. As a result, a self-adaptive spectral clustering algorithm based on affinity matrix is proposed. The experimental results on the UCI data sets show that the algorithm is more effective than previous algorithms.
Keywords
eigenvalues and eigenfunctions; pattern clustering; UCI data sets; affinity matrix; eigenvectors; self-adaptive spectral clustering algorithm; Clustering algorithms; Clustering methods; Eigenvalues and eigenfunctions; Euclidean distance; Graph theory; Joining processes; Laplace equations; Partitioning algorithms; Number of the clusters; Self-adaptive; Spectral Clustering;
fLanguage
English
Publisher
ieee
Conference_Titel
Control Conference, 2008. CCC 2008. 27th Chinese
Conference_Location
Kunming
Print_ISBN
978-7-900719-70-6
Electronic_ISBN
978-7-900719-70-6
Type
conf
DOI
10.1109/CHICC.2008.4605517
Filename
4605517
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