XML clustering ensemble based on quantum genetic algorithm
基于量子遗传算法的XML聚类集成

To improve the clustering performance of a single clustering algorithm, this paper proposed an approach of the XML document clustering ensemble algorithm based on quantum genetic algorithm. Firstly, it divided the XML document into the difference of the k-members clustering using the K-nearest neighbour classifier algorithm. Next according to the relationship between the clustering members of the datasets was obtained Co-occurrence similarity matrix, and through a multi-segment and upward and downward double-direction shrink QR algorithm decomposition a large-scale matrix of eigenvalue to achieve the corresponding eigenvector matrix of dimensionality reduction. Finally in mapping space, using the quantum genetic algorithm to complete clustering ensemble, and discriminate the optimal clustering category from each sample. For to do it that would be reduced the data differences on the impact of clustering effects, and improved the clustering quality. Experiments on real-world data sets indicate that it has better clustering effects than clustering ensemble algorithms.