基于Hausdorff距离的关系层次聚类算法

The clustering algorithm of relational data is very important for communication research. Firstly, uses the iterative system to metaphorize the individual structure changing, expresses the distance between the output and the state, and also uses the node with the highest structural level sequence to represent the cluster; then introduces the Hausdorff distance into the DBSCAN algorithm. Thus, the summation operator that merges with the same structure and the union operator of the scale on the hierarchy become compressible. We use the data of complex network researchers to evaluate the effectiveness of the algorithm. The layer of cooperation network has different network structure; the keywords have high transmission efficiency in the level 2; the reciprocal relationship has the most effecton knowledge dissemination. These new findings prove that the algorithm can reflect the influence of the network structure on the propagation effect by introducing the compressible measure Hausdorff distance of the Hutchinson operator. The design idea of the algorithm is correct.