An Efficient Chain Structure to Mine High-Utility Sequential Patterns

Jerry Chun-Wei Lin, Yuanfa Li, Philippe Fournier-Viger, Youcef Djenouri, Ji Zhang

Abstract: High-utility sequential pattern mining (HUSPM) is an emerging topic in data mining, which considers both utility and sequence factors to derive the set of high-utility sequential patterns from the quantitative databases. Several works have been presented to speed up computational cost by variants of pruning strategies. In this paper, we present an efficient sequence-utility (SU)-chain structure, which can be used to store more relevant information to improve mining performance. Based on the SU-Chain structure, the existing pruning strategies can also be utilized here to early prune the unpromising candidates and obtain the satisfied HUSPs. Experiments are then compared with the state-of-the-art USpan, HUS-Span and HUSP-ULL approaches and the results of the developed structure can achieve good mining performance than the existing algorithms.