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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.
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.
The oddball sequence included 2 infrequent (probability = 0.1 for each) sounds and 1 frequent (probability = 0.8) sound.
So, frequent probability pattern recognition in biological networks is an important step in identifying the probability motif.
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Medium and low scores were the most frequent probabilities assigned to the interventions.
where P good|frequent groups) is the conditional probability, frequent groups ∪ good or bad means the union of frequent groups and good or bad performance, and supp(frequent groups ∪ good or bad) stands for the counts of frequent groups with good or bad performance occurring in transactions.
Fuller et al. (2002) reason that, for frequent acquirers, the probability is quite high that previous takeover attempts will be included in the estimation period, thus making beta estimations less meaningful.
However, for frequent dedifferentiation, the probability of symmetric divisions evolved significantly, but by very small amounts.
This paper presents a probability frequent subgraph mining algorithm in biological probabilistic networks based on the circuit simulation method.
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