Fuzzy utility (FU) pattern mining with an advantage in human reasoning has become one of the interesting topics in studies of knowledge discovery. The discovered information in FU pattern mining from real-life quantitative databases with item profits is suitable for interpreting data from a human perspective because it is not expressed using numerical values but linguistic terms which consist of natural languages. State-of-the-art approaches in this literature provide extended results by considering temporal factors, such as seasons, which can be influential in real-life situations. However, they still suffer from scalability issues because they are based on level-wise approaches which generate a number of candidates. In this article, we propose a scalable and efficient approach with a novel data structure for mining high temporal FU patterns without generating candidates. Efficient pruning techniques and algorithms are presented to improve the performance of the proposed approach. Performance experiments on both real and synthetic datasets show that the suggested algorithm has better performance than the state-of-the-art algorithms in terms of runtime, memory usage, and scalability.

中文翻译：

---

用于高时态模糊效用模式挖掘的可扩展且高效的方法。

具有人类推理优势的模糊效用（FU）模式挖掘已成为知识发现研究中有趣的课题之一。FU模式挖掘从具有项目利润的现实定量数据库中发现的信息适合从人类的角度解释数据，因为它不是使用数值而是使用由自然语言组成的语言术语来表达。该文献中最先进的方法通过考虑时间因素（例如季节）来提供扩展的结果，这些因素在现实生活中可能会产生影响。然而，它们仍然面临可扩展性问题，因为它们基于生成大量候选者的逐级方法。在本文中，我们提出了一种可扩展且高效的方法，采用新颖的数据结构来挖掘高时间 FU 模式，而无需生成候选者。提出了有效的修剪技术和算法来提高所提出方法的性能。在真实数据集和合成数据集上的性能实验表明，所提出的算法在运行时、内存使用和可扩展性方面比最先进的算法具有更好的性能。