Improvement on Muti-way Spectral Clustering Algorithm for Complex Distributed Data

To cluster the complex structure dataset effectively using multi-way spectral clustering,based on the matrix perturbation theory,the initial similarity matrix in Ng-Jordan-Weiss(NJW) algorithm was updated by using local neighbor relation and then the last affinity matrix was gained.Theoretical analysis showed that this last affinity matrix was ideal block matrix or near ideal block matrix so that it could make the clustering correct.The method was compared with path-based spectral clustering,density-sensitive spectral clustering and spectral clustering through ranking on manifolds together.Result illustrates that the affinity matrix can decide the clustering number so as to get the correct clustering result.Further,the real dataset is used to check our method,and the result shows that the method is effective.