  High-utility sequential pattern mining is an emerging topic in the field of
Knowledge Discovery in Databases. It consists of discovering subsequences
having a high utility (importance) in sequences, referred to as high-utility
sequential patterns (HUSPs). HUSPs can be applied to many real-life
applications, such as market basket analysis, E-commerce recommendation,
click-stream analysis and scenic route planning. For example, in economics and
targeted marketing, understanding economic behavior of consumers is quite
challenging, such as finding credible and reliable information on product
profitability. Several algorithms have been proposed to address this problem by
efficiently mining utility-based useful sequential patterns. Nevertheless, the
performance of these algorithms can be unsatisfying in terms of runtime and
memory usage due to the combinatorial explosion of the search space for low
utility threshold and large databases. Hence, this paper proposes a more
efficient algorithm for the task of high-utility sequential pattern mining,
called HUSP-ULL. It utilizes a lexicographic sequence (LS)-tree and a
utility-linked (UL)-list structure to fast discover HUSPs. Furthermore, two
pruning strategies are introduced in HUSP-ULL to obtain tight upper-bounds on
the utility of candidate sequences, and reduce the search space by pruning
unpromising candidates early. Substantial experiments both on real-life and
synthetic datasets show that the proposed algorithm can effectively and
efficiently discover the complete set of HUSPs and outperforms the
state-of-the-art algorithms.
