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论文类型：期刊论文

发表时间：2012-01-01

发表刊物：ICIC Express Letters, Part B: Applications

收录刊物：EI、Scopus

卷号：3

期号：5

页面范围：1147-1155

ISSN号：21852766

摘要：This paper proposes a novel frequent itemsets mining algorithm based on transaction matrix, called TMA, to improve the efficiency of finding frequent itemsets for personalized recommendations. By constructing a data structure named Transaction Matrix, TMA creates candidate itemsets by scanning database only once. Meanwhile, a series of processes composed of AND and XOR operations is designed to generate candidate itemsets, which can compress candidate searching space and avoid redundant candidates by deleting infrequent itemsets during mining frequent itemsets. To evaluate the performance of our algorithm, experiments are carried out on four different datasets, and the results show that TMA outperforms three other algorithms in terms of the efficiency. Thus, TMA has a better applicability for precise personalized recommendation. ? 2012 ICIC International.