MINING FREQUENT PATTERNS BASED ON INVERTED LIST

摘要：

In this paper, an Apriori algorithm is presented for mining frequent patterns based on inverted list. Compared with traditional Apriori algorithm and FP-growth algorithm, this algorithm has better efficiency and wider application range. Aimed at reducing the defect of traditional Apriori algorithm, this algorithm avoids lots of redundant operations with inverted list. This algorithm only needs scan data set twice and dont need joining and pruning operations.Frequent item set is saved in each transaction frequent set TF,and insert next frequent single item one by one,then generate new possible frequent item set. In this way, lots of redundant operations can be reduced. The performance study shows that it is more efficient in both dense datasets and sparse datasets.

关键词： Data mining frequent patterns Apriori inverted list

作者: YONG LIU YUN-FA HU

作者单位: Computer and Information Technology Department, Fudan University, Shanghai 200433, China

会议类型: 国际会议

会议名称: 2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

会议地点: 大连

会议语种:英文

页码: 1320-1325

在线出版日期: 2006-08-13（万方平台首次上网日期，不代表论文的发表时间）