Towards high-utility sequential rules with repetitive items

Hong Lin, Wensheng Gan, Gengsen Huang, Philip S. Yu

Abstract: Discovering sequential rules in the sequence database is of key importance for a variety of fields, ranging from customer behavior analysis to intrusion detection. To obtain more informative rules, high-utility sequential rule mining (HUSRM) was proposed. Its goal is to find those sequential rules with high utility values and high confidence, i.e., HUSRs. As far as we know, there are a few algorithms proposed to discover HUSRs. However, these algorithms do not fully consider the existence of repeated items in the sequences of the database. In this paper, we propose an algorithm named USER to discover HUSRs in multi-sequences with the existence of repeated items. A data structure called an occurrence information (OI)-list is designed to distinguish the different occurrences of items in a sequence. Moreover, the change of the upper bound value after the rule expansion is discussed in detail, which is complicated by the repeated items. We also propose four pruning strategies (ROOR, REIO-I, REIO-II, and LEIO) to optimize mining efficiency when there are too many repeated items in the sequence. Finally, we conduct experiments on several datasets, and the results show that USER can mine HUSRs with more accurate utility values in an acceptable amount of time and memory.

External IDs:dblp:journals/mlc/LinGHY25