  Utility mining has emerged as an important and interesting topic owing to its
wide application and considerable popularity. However, conventional utility
mining methods have a bias toward items that have longer on-shelf time as they
have a greater chance to generate a high utility. To eliminate the bias, the
problem of on-shelf utility mining (OSUM) is introduced. In this paper, we
focus on the task of OSUM of sequence data, where the sequential database is
divided into several partitions according to time periods and items are
associated with utilities and several on-shelf time periods. To address the
problem, we propose two methods, OSUM of sequence data (OSUMS) and OSUMS+, to
extract on-shelf high-utility sequential patterns. For further efficiency, we
also designed several strategies to reduce the search space and avoid redundant
calculation with two upper bounds time prefix extension utility (TPEU) and time
reduced sequence utility (TRSU). In addition, two novel data structures were
developed for facilitating the calculation of upper bounds and utilities.
Substantial experimental results on certain real and synthetic datasets show
that the two methods outperform the state-of-the-art algorithm. In conclusion,
OSUMS may consume a large amount of memory and is unsuitable for cases with
limited memory, while OSUMS+ has wider real-life applications owing to its high
efficiency.
