Maximal Frequent Itemsets Mining Algorithm Based on Linked List Array
Xiaoyun Chen · Jisuanji gongcheng · 2010
Mining all frequent itemsets in dense datasets is very expensive. Aiming at this problem, linked list array, a new data structure, and a fast method of Mining Frequent Itemsets(MFI) based on it are proposed. This method creates linked list array for each item, only needs scan database one time, uses depth-first search strategy to generate all MFI. The algorithm reduces search space by using constraint condition. It demonstrates the algorithm with standard dataset, and the experimental results confirm that the mining algorithm can significantly improve the speed of mining MFI compared with other algorithms.