Maximal Frequent Itemsets in Data Stream Mining Based on Orderly-Compound Policy

摘要：

Mining maximal frequent itemsets get the advantage of a relatively small number of itemsets. Compared to mining frequent itemsets and mining frequent closed itemsets, such algorithm has higher time and space efficiency. According to the features of data streams and combined sliding window, a new algorithm E-FPMFI which is based on orderly-compound policy for mining maximal frequent itemsets in data stream is proposed. The algorithm based on basic window updates information from data stream flow fragment and scans the stream only once to gain and store it in frequent itemsets list. The algorithm construct FP-tree, then compress orderly FP-tree by merging nodes which has equal minsup in same branch, also uses subset mix pruning technique, avoid superset checking. The experimental results show the algorithm has higher time, space efficiency and good scalability.

关键词： frequent itemsets sliding window orderly-compound FP-tree

作者: PeiShuai Chen ChongHuan Xu

作者单位: College of Computer Science & Information, Zhejiang Gongshang University, Hangzhou, China Business Administration college of Zhejiang Gongshang University Hangzhou,China

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: 113-117

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）