New Policy of Maximal Frequent Itemsets in Data Stream Mining

摘要：

According to the features of data streams and combined sliding window, a new algorithm A-MFI which is based on self-adjusting and orderly-compound policy for mining maximal frequent itemsets in data stream is proposed. This algorithm which is based on basic window updates information from data stream flow fragments and scans the stream only once to gain and store it in frequent itemsets list when the data stream flows. The core idea of this algorithm: construct self-adjusting and orderly-compound FPtree, use mixed subset pruning techniques to reduce the search space, merge nodes which has equal minsup in the same branch and compress to generate the orderly-compound FP-tree to avoid superset checking when mining maximal frequent itemsets. The experimental results show that the algorithm has higher efficiency in time and space, and also has good scalability.

关键词： data stream maximal frequent itemsets self-adjusting orderly-compound FP-tree

作者: ChongHuan Xu ChunHua Ju

作者单位: Business Administration college of Zhejiang Gongshang University Hangzhou,China Center for Studies of Modern Business Zhejiang Gongshang University Hangzhou,China College of Comput

会议类型: 国际会议

会议名称: 2010 International Conference on Advanced Mechanical Engineering(2010年先进机械工程国际学术会议 AME 2010)

会议地点: 洛阳

会议语种:英文

页码: 118-122

在线出版日期: 2010-09-04（万方平台首次上网日期，不代表论文的发表时间）