An Improved Apriori Algorithm Based on Matrix
Liu Shuwen, Jiyi Xiao · 2020
This paper analyzes the limitations of the Apriori algorithm for mining frequent itemsets with low time and space efficiency, and proposes a frequent itemset mining algorithm for matrix, itemset count and item index list. It only needs to scan the database once. According to the prior nature of frequent itemsets, the size of the data scan is reduced by compressing the matrix, and then the bitwise and operation is performed on the compressed matrix row vectors. The itemset count and prefix index list are used to generate frequent itemsets. No candidate set is generated during the mining process. This algorithm improves the efficiency of the original algorithm.