Extraction of Belief Knowledge from a Relational Database for Quantitative Bayesian Network Inference

Algorithm 1

𝐶_𝑘: candidate item set of size 𝑘

𝐿_𝑘: frequent item set of size 𝑘

𝐿₁ = frequent items;

For (𝑘=1;𝑘≠𝜙;𝑘++) do begin

 𝐶_𝑘+1 = candidates generated from 𝐿_𝑘;

 for each transaction 𝑡 in database do

 increment the count of all candidates in 𝐶_𝑘+1 that are contained in 𝑡

 𝐿_𝑘+1 = candidates in 𝐶_𝑘+1 with min support

end

return ∪_𝑘  𝐿_𝑘;