面向移动 App 流量的多特征集合集成 聚类方法研究与应用.

To handle the mobile traffic identification problem, based on multiple performance evaluation metrics, this paper analyzed the performance of K-means and spectral clustering algorithms on the data sets characterized by different feature sets or labeled with different class set, and proposed an ensemble clustering method from the aspects of combining the clustering results on the data sets with different feature sets. Experimental results show that the performance of the same clustering algorithm is different on the data sets with different feature sets or traffic classes, and the ensemble clustering method is able to improve the overall clustering performance. Further, this paper applied the ensemble clustering method on the correlation analysis of mobile Apps, and the results can support the decision on grouping Apps and analyzing user behaviors. [ABSTRACT FROM AUTHOR]