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Medium and low scores were the most frequent probabilities assigned to the interventions.
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(3) Frequent probability subgraph discovered by two-step hierarchical clustering.
A key step of identifying probability motif is the mining of frequent probability pattern.
A probability frequent subgraph mining algorithm includes the circuit simulation method to evaluate probability isomorphism and two-step hierarchical clustering to recognize frequent probability pattern.
So, frequent probability pattern recognition in biological networks is an important step in identifying the probability motif.
The oddball sequence included 2 infrequent (probability = 0.1 for each) sounds and 1 frequent (probability = 0.8) sound.
Experimental results also indicate that as the clustering threshold mismatch decreases, the number of clustered subgraphs increases, and the frequency of frequent probability pattern similar to motif was gradually reduced, the time of recognizing frequent probability subgraph was increasing.
The pseudocode of the algorithm of frequent probability subgraph discovered by two-step hierarchical clustering is shown in Table 2.
Furthermore, a frequent probability pattern recognition algorithm based on two-step hierarchical clustering was also proposed for better recognition performance.
Based on the method of probability subgraph isomorphic, the frequent probability pattern can be identified from the probability subgraph set using graph alignment.
Frequent probability pattern mining, a key step in the probability motif identification, is based on the method of probability isomorphic evaluation.
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