Efficient Sequential Pattern Mining Algorithm by Positional Data

Abstract

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 Prefix Span 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 depth-first 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.