关系数据的聚类算法对于传播研究意义重大,首先运用迭代系统隐喻个体结构的变化,用输出与状态的包含距离表示关系的非对称同时也确定拥有最高结构等级序列的节点来代表簇;再将Hausdorff距离引入DBSCAN算法,使得同结构节点进行合并的加和算子和层次上卷的并算子变得可压缩。运用复杂网络研究人员的数据对算法的有效性进行了评估,分层后的人员合作网具有不同的网络结构特征;关键词在层次2网络中的传播效率高;互惠关系在知识传播中的作用最大。新的发现证明算法通过引入Hutchinson算子的可压缩测度Hausdorff距离使得网络结构对传播效果的影响得以体现,该算法的设计思路是正确的。
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.
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