A Spectral Clustering Algorithm Based on Normalized Cuts
Peng Yang, Biao Huang · 2008
Recently, spectral clustering has wide application in pattern recognition and data mining because it can obtain global optima solution and adapt to sample spaces with any shape. Thus, a spectral clustering algorithm based on normalized cuts is proposed in this paper. It selects the k eigenvalues and corresponding eigenvectors of a given stochastic matrix and clusters in n times k sub-space. Experimental results show that it has better performance comparing with the traditional clustering algorithm.