DocumentCode
2991247
Title
Normalizing Modularity Matrices for Data Clustering
Author
Wang, Rong
Author_Institution
Center of network Eng. Technol., Weinan Teachers Univ., Weinan, China
fYear
2011
fDate
3-4 Dec. 2011
Firstpage
1328
Lastpage
1330
Abstract
Inspired by the fact that normalized graph Laplacian is superior to the non-normalized one in spectral clustering, in this paper we develop a new symmetric matrix called normalized modularity matrix which has different properties with the normalized graph Laplacian. And thus a new spectral clustering algorithm is derived based on this new matrix. Experimental results show the performance of the proposed algorithm: (1) being helpful in detecting intrinsic cluster number, (2) being competitive to two baseline algorithms.
Keywords
data handling; graph theory; matrix algebra; pattern clustering; spectral analysis; baseline algorithm; data clustering; intrinsic cluster detection; normalized graph Laplacian; normalized modularity matrix; spectral clustering algorithm; symmetric matrix; Algorithm design and analysis; Clustering algorithms; Educational institutions; Eigenvalues and eigenfunctions; Iris; Partitioning algorithms; Symmetric matrices; Graph; Normalized matrix of modularity; Spectral clustering;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Intelligence and Security (CIS), 2011 Seventh International Conference on
Conference_Location
Hainan
Print_ISBN
978-1-4577-2008-6
Type
conf
DOI
10.1109/CIS.2011.295
Filename
6128336
Link To Document