The application of parallel clustering analysis based on big data mining in physical community discovery

To improve the performcance of community discovery algorithm applied to dynamic community detection objects, a parallel clustering analysis based on packet permission hierarchical association mining in community discovery of big data has been proposed. First, an evolutionary non-negative matrix decomposition framework based on clustering quality is proposed for dynamic community detection. Second, a clustering combined with dynamic pruning binary tree support vector machine (SVM) algorithm is proposed to prove the equivalence between evolutionary binary tree clustering and evolutionary module density optimization from the perspective of theoretical analysis. Based on this equivalence, a new semi-supervised association mining algorithm is proposed by adding prior information to the sample data without increasing the time complexity. Finally, through the experimental analysis on the static and dynamic community detection model, the performance advantage of the proposed algorithm on the community detection performance index is verified.