Real-world data usually comprise features whose interpretation depends on some contextual information. Such contextual-sensitive features and patterns are of high interest to be discovered and analyzed in order to obtain the right meaning. This paper formulates the problem of mining context-aware association rules, which refers to the search for associations between itemsets such that the strength of their implication depends on a contextual feature. For the discovery of this type of associations, a model that restricts the search space and includes syntax constraints by means of a grammar-based genetic programming methodology is proposed. Grammars can be considered as a useful way of introducing subjective knowledge to the pattern mining process as they are highly related to the background knowledge of the user. The performance and usefulness of the proposed approach is examined by considering synthetically generated datasets. A posteriori analysis on different domains is also carried out to demonstrate the utility of this kind of associations. For example, in educational domains, it is essential to identify and understand contextual and context-sensitive factors that affect overall and individual student behavior and performance. The results of the experiments suggest that the approach is feasible and it automatically identifies interesting context-aware associations from real-world datasets.

中文翻译：

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使用基于语法的遗传编程挖掘上下文感知关联规则


现实世界的数据通常包含其解释取决于某些上下文信息的特征。为了获得正确的含义，这种上下文敏感的特征和模式值得被发现和分析。本文提出了挖掘上下文感知关联规则的问题，它是指搜索项集之间的关联，使得其蕴含的强度取决于上下文特征。为了发现这种类型的关联，提出了一种通过基于语法的遗传编程方法来限制搜索空间并包括语法约束的模型。语法可以被认为是将主观知识引入模式挖掘过程的有用方法，因为它们与用户的背景知识高度相关。通过考虑综合生成的数据集来检查所提出方法的性能和实用性。还对不同领域进行了后验分析，以证明这种关联的实用性。例如，在教育领域，识别和理解影响整体和个人学生行为和表现的情境和情境敏感因素至关重要。实验结果表明该方法是可行的，它可以自动从现实世界的数据集中识别有趣的上下文感知关联。