Research on Mining Maximum Frequent Itemsets Based on JFP-Growth Algorithm

摘要：

　　The FP-Growth algorithm is the most representative frequent item set mining algorithm.An improved algorithm JFP-Growth algorithm is proposed for the shortcomings of FP-Growth algorithm.When mining the maximum frequent itemsets,the JFP-Growth algorithm traverses the support of the first 1-item set and the 2-item set of the first-time data set statistics,and uses the frequent 2-item set as the pruning condition for full pruning and The merged nodes make there is no non-potential candidate 3-item set in the JFP-tree,and the conditional pattern base is generated without traversing the project header table during the mining process.Finally,the FP-Growth algorithm and DMFIA algorithm are compared in the mining results,the number of nodes in FP-tree and JFP-tree,mining efficiency,etc.,which verifies the correctness and efficiency of the JFP-Growth algorithm proposed in this paper.

关键词： frequent itemsets FP-Growth algorithm,frequent 2-item set,pruning,two-dimensional count table

作者: Wang Zeru Wang Hongmei

作者单位: School of Computer Science and Engineering,Changchun University of Technology,Changchun,China

会议类型: 国际会议

会议名称: 2019 2nd International Conference on Mechanical, Electronic and Engineering Technology (MEET 2019) 2019年第二届机电与工程技术国际会议

会议地点: 西安

会议语种:英文

页码: 33-39

在线出版日期: 2019-01-19（万方平台首次上网日期，不代表论文的发表时间）