Mining Sequential Association Rules Efficiently by Using Prefix Projected Databases
Yi‐Chun Chen, Guan-Ling Lee · 2011
The mining of sequential patterns has been studied for several years, however, there is little studies pay attention to mining of sequential association rules despite such rules also providing valuable knowledge about many real applications. The sequential association rule represents the concept that a set of items usually occur after a specific order sequence. In this paper, the idea of sequential association rule is introduced and two algorithms, GSAR and PSAR algorithms, are proposed to discover these hidden knowledge. Moreover, experiments are performed on both synthetic and real datasets to show the benefit of our approach.