Considering the high complexity of HTE(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.