A fast algorithm for maximum frequent itemsets based on the user' interest using FP-matrix

Mining maximum frequent item sets is crucial for mining association rules. This paper proposes a novel algorithm, interest frequent pattern matrix(IFPM), which is based on user´s interests, and illustrates the algorithm´s execution process. IFPM preprocesses and filters the transaction database according to the level of data item and user´s interests, making the handling data reduce an order of magnitude. And then scans the filtered database to create a FP-Matrix, searches the FP-Matrix by top-down depth-first, which can produce maximum frequent item sets(MFI), frequent item sets(FI)and Closed frequent item set(CFI) by vector operation, thus greatly improves the algorithm´s efficiency.