文献类型：会议论文

英文题名：Mining Top-K Closed Frequent Traversal Sequences from Session Streams

作者：Zhang, Xiaojian[1];Peng, Huili[1];Shao, Chao[1]

第一作者：张啸剑

通讯作者：Zhang, XJ[1]

机构：[1]Henan Univ Finance & Econ, Dept Comp Sci, Zhengzhou, Peoples R China

第一机构：河南财经政法大学计算机与信息工程学院

通讯机构：[1]corresponding author), Henan Univ Finance & Econ, Dept Comp Sci, Zhengzhou, Peoples R China.|[1048412]河南财经政法大学计算机与信息工程学院;[10484]河南财经政法大学;

会议论文集：5th International Conference on Fuzzy Systems and Knowledge Discovery

会议日期：OCT 18-20, 2008

会议地点：Jinan, PEOPLES R CHINA

语种：英文

外文关键词：Fuzzy logic - Fuzzy systems - Tubular steel structures

摘要：Approximate mining top-k closed frequent traversal sequence (Topk_CFTS) has two methods, namely, false-positive oriented and false-negative oriented. Most false-positive approaches require relaxation ratio p to control memory consumption and mining accuracy. However, it is difficult for users to provide a proper p value. A higher p may reduce output precision, and a smaller p will make memory consumption large. To resolve the conflict, this paper designs a regulatory factor to adjust p value, and proposes an algorithm, TStream, based on false-negative method to maintain the set of Topk_CFTS in session streams with time-sensitive sliding windows. Experiments show that TStream algorithm performs much better than many established algorithms for mining Topk_CFTS.