Abstract
Sequential rule mining is an important data mining technique that discovers relationships between occurrences of sequential patterns. The main challenge is to avoid time-consuming, especially in large search spaces. This can be achieved through developing an efficient sequential rule mining algorithm without redundancy in a long sequence dataset. In this paper, an algorithm named PNRD-CloGen is proposed. It can be used to mine sequential rules from both frequent closed dynamic bit vectors with sequential generator patterns at the same time. It helps in speedily eliminating uninteresting candidates and compact the representations. Additionally, we apply a parallel approach utilizing multi-core architecture. An experimental evaluation was performed using five real sequence datasets: BMSWebView1, Sign, FIFA, Korsarak, and MSNBC. The proposed algorithm has been compared with two non-redundant sequential rule algorithms called: TRuleGrowth, and NRD-DBV algorithm. Experimental results show the time saving, especially for large sequence datasets and low minimum support threshold.