Mining Sequential Rules Based on Prefix-Tree

Abstract

We consider the problem of discovering sequential rules between frequent sequences in sequence databases. A sequential rule expresses a relationship of two event series happening one after another. As well as sequential pattern mining, sequential rule mining has broad applications such as the analyses of customer purchases, web log, DNA sequences, and so on. In this paper, for mining sequential rules, we propose two algorithms, MSR_ImpFull and MSR_PreTree. MSR_ImpFull is an improved algorithm of Full (David Lo et al., 2009), and MSR_PreTree is a new algorithm which generates rules from frequent sequences stored in a prefix-tree structure. Both of them mine the complete set of rules but greatly reduce the number of passes over the set of frequent sequences which lead to reduce the runtime. Experimental results show that the proposed algorithms outperform the previous method in all kinds of databases.