Research Article
FraudMiner: A Novel Credit Card Fraud Detection Model Based on Frequent Itemset Mining
Algorithm 1
Training algorithm.
Input: Customer Transactions Database , support | Output: Legal Pattern Database LPD, Fraud Pattern Database FPD | Begin | Group the transactions of each customer together. | Let there are “” groups corresponds to “” customers | for to do | Separate each group Gi into two different groups LGi | and FGi of legal and fraud transactions. Let there | are “” legal and “” fraud transactions | FIS = Apriori(LGi, , ); //Set of frequent itemset | LP = ; //Large Frequent Itemset | LPD() = LP; | FIS = Apriori(FGi, , ); //Set of frequent itemset | FP = ; //Large Frequent Itemset | FPD() = FP; | endfor | return LPD & FPD; | End |
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