Frequent Itemsets Mining Algorithm Based on Matrix
Yang Jing · Jisuanji gongcheng · 2009
How to mine the frequent itemsets efficiently is a main problem in association rule mining. According to the theory of congregation and matrix, a frequent itemsets mining algorithm based on matrix is proposed. Through scanning database only once, all transactions are transformed to be rows of matrix and all items and itemsets are transformed to be columns of matrix. This algorithm can one-off product all frequent itemsets, and need not rescan the database when support threshold value changes. Experimental results show the mining efficiency of this algorithm is higher than Apriori algorithm.