Abstract Since the corresponding eigenvectors of k maximum eigenvalues do not always achieve the optimal clustering results, the clustering performance is improved by selective integrated approach for eigenvector groups involving the selection of base eigenvector group and selective integration strategy. Constraint score is used to evaluate eigenvectors by the pair-wise constraint information of training data, and some preferable base eigenvector groups are obtained. For each testing data, the clustering accuracy of l-nearest neighbors from training dataset are used to dynamically evaluate eigenvector groups, and several accurate eigenvector groups are selected to vote. To test the obtained eigenvector groups, spectral clustering is carried out on the corresponding eigenvectors of testing dataset. The clustering results are aligned and the final experimental results are obtained. The experimental results on UCI benchmark datasets show that the proposed algorithm improves the clustering performance of testing data.
2014, Vol. 27 
