Efficient Sequential Pattern Mining Algorithm by Positional Data
The CloSpan algorithm first suggested that the closed set of sequential patterns is more compact and has the same expressive power with respect to the full set.Based on the PrefixSpan algorithm,CloSpan added two pruning techniques,backward sub-pattern and backward super-pattern,to efficiently mine the closed set.This paper proposed a new closed sequential pattern mining algorithm.However,instead of depthfirst searching used in many previous methods,we adopt a breadth-first approach.Besides,previous methods seldom utilize the property of item ordering to enhance efficiency.We used a list of positional data to reserve the information of item ordering.By using these positional data,we developed two main pruning techniques,backward super-pattern condition and same positional data condition.To ensure correct and compact resulted lattice,we also manipulated some special conditions.From the experimental results,our algorithm outperforms CloSpan in the cases of moderately large datasets and low support threshold.
data mining sequential pattern closed sequential pattern backward super-patter
Sha Jin Hu Yingxin Jia Lianjuan
Department of Information Science and Technology Shijiazhuang Tiedao University Shijiazhuang,Hebei P Shijiazhuang Jiechengl BuildingDecoration Engineering Co.Ltd.Shijiazhuang,Hebei Province,China
国际会议
秦皇岛
英文
64-67
2010-11-05（万方平台首次上网日期，不代表论文的发表时间）