Agglomerative hierarchical clustering based algorithm for network topology inference

被引：0

作者：

Zhang, Run-Sheng ^{[1
]}

Li, Yan-Bin ^{[1
]}

Li, Xiao-Tian ^{[1
]}

机构：

[1] The 54th Research Institute of CETC, Shijiazhuang

来源：

Zhang, R.-S. (zhang_runsheng@163.com) | 2013年 / Chinese Institute of Electronics卷 / 41期

关键词：

Expectation maximization; Finite mixture model; Hierarchical clustering; Topology inference;

D O I：

10.3969/j.issn.0372-2112.2013.12.005

中图分类号：

学科分类号：

摘要：

Considering the high complexity of THE (Hierarchical Topology Estimation) and its performance degradation under the condition of large correlation estimation variance, a method based on agglomerative hierarchical lustering is proposed. The method employs bottom-up agglomerative hierarchical clustering, which only uses the data related to the node pair with the largest correlation, so it has lower computation complexity than HTE. A modified finite mixture model is established, increasing the amount of effective data, which improves the accuracy of parameter estimation. The simulation demonstrates that the proposed method infers the topology more rapidly, with higher accuracy when the correlation estimation variance is large.

引用

页码：2346 / 2352

页数：6