Title :
Normalizing Modularity Matrices for Data Clustering
Author_Institution :
Center of network Eng. Technol., Weinan Teachers Univ., Weinan, China
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;
Conference_Titel :
Computational Intelligence and Security (CIS), 2011 Seventh International Conference on
Conference_Location :
Hainan
Print_ISBN :
978-1-4577-2008-6
DOI :
10.1109/CIS.2011.295
