DocumentCode :
2540735
Title :
A Note on Spectral Clustering Method Based on Normalized Cut Criterion
Author :
Sumuya ; Guo, Chonghui ; Chai, Shanglei
Author_Institution :
Inst. of Syst. Eng., Dalian Univ. of Technol., Dalian, China
fYear :
2009
fDate :
4-6 Nov. 2009
Firstpage :
1
Lastpage :
5
Abstract :
Recently spectral clustering has become one of the most popular clustering algorithms. Although it has many advantages, it still has a lot of shortcomings which should be resolved, such as there are a wide variety of spectral clustering algorithms that use the eigenvectors in slightly different ways and many of these algorithms have no proof that they will actually compute a reasonable clustering. The spectral clustering method based on normalized cut criterion is a very efficient spectral clustering method. In this paper, we give a note on why we choose the first k eigenvectors in the algorithm (rationality of the clustering) and the conditions for indicator vectors under which the clustering problem could lead to the problem of minimizing the objective function of the spectral clustering method based on normalized cut criterion.
Keywords :
eigenvalues and eigenfunctions; graph theory; pattern clustering; eigenvector; indicator vector; normalized cut criterion; spectral clustering algorithm; spectral clustering method; Astronomy; Biomedical engineering; Clustering algorithms; Clustering methods; Computer science; Eigenvalues and eigenfunctions; Graph theory; Partitioning algorithms; Shape; Systems engineering and theory;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition, 2009. CCPR 2009. Chinese Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4244-4199-0
Type :
conf
DOI :
10.1109/CCPR.2009.5343984
Filename :
5343984
Link To Document :
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