Frequent itemset mining (FIM) is an essential task within data analysis since it is responsible for extracting frequently occurring events, patterns, or items in data. Insights from such pattern analysis offer important benefits in decision‐making processes. However, algorithmic solutions for mining such kind of patterns are not straightforward since the computational complexity exponentially increases with the number of items in data. This issue, together with the significant memory consumption that is present in the mining process, makes it necessary to propose extremely efficient solutions. Since the FIM problem was first described in the early 1990s, multiple solutions have been proposed by considering centralized systems as well as parallel (shared or nonshared memory) architectures. Solutions can also be divided into exhaustive search and nonexhaustive search models. Many of such approaches are extensions of other solutions and it is therefore necessary to analyze how this task has been considered during the last decades.

中文翻译：

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频繁项集挖掘：25年回顾

频繁项集挖掘（FIM）是数据分析中的一项基本任务，因为它负责提取数据中频繁发生的事件，模式或项。这种模式分析的见解在决策过程中提供了重要的好处。但是，用于挖掘此类模式的算法解决方案并不简单，因为计算复杂度会随着数据项的数量呈指数增长。此问题以及挖掘过程中存在的大量内存消耗使必须提出极其有效的解决方案。自从1990年代初首次描述FIM问题以来，已经通过考虑集中式系统以及并行（共享或非共享内存）体系结构提出了多种解决方案。解决方案也可以分为穷举搜索和非穷举搜索模型。许多这样的方法是其他解决方案的扩展，因此有必要分析最近几十年来如何考虑此任务。