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 article, 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 design 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 are 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.

中文翻译：

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序列数据的现成实用程序挖掘

公用事业采矿由于其广泛的应用和相当受欢迎的程度而成为一个重要而有趣的话题。然而，传统的效用挖掘方法偏向于具有较长货架时间的物品，因为它们有更大的机会产生高效用。为了消除偏差，引入了现成效用挖掘（OSUM）问题。在本文中，我们重点关注序列数据的 OSUM 任务，其中序列数据库根据时间段划分为多个分区，项目与实用程序和几个上架时间段相关联。为了解决这个问题，我们提出了两种方法，序列数据的 OSUM (OSUMS) 和 OSUMS+，以提取现成的高效用序列模式。为了进一步提高效率，我们还设计了几种策略来减少搜索空间并避免使用两个上限时间前缀扩展实用程序（TPEU) 和时间缩减的序列效用 (TRSU）。此外，还开发了两种新的数据结构以方便计算上限和效用。在某些真实和合成数据集上的大量实验结果表明，这两种方法优于最先进的算法。综上所述，OSUMS 可能会消耗大量内存，不适合内存有限的情况，而 OSUMS+由于其高效率而具有更广泛的实际应用。