Revealing complex relations between entities (e.g., items within or between transactions) is of great significance for business optimization, prediction, and decision making. Such relations include not only co-occurrence-based explicit relations but also nonco-occurrence-based implicit ones. Explicit relations have been substantially studied by rule mining-based approaches, including association rule mining and causal rule discovery. In contrast, implicit relations have received much less attention but could be more actionable. In this paper, we focus on the implicit relations between items which rarely or never co-occur while each of them co-occurs with other identical items (link items) with a high probability. A framework integrates both explicit and hidden item dependencies and a corresponding efficient algorithm IRRMiner captures such implicit relations with implicit rule inference. Experimental results show that IRRMiner not only infers implicit rules of various sizes consisting of both frequent and infrequent items effectively, it also runs at least four times faster than IARMiner, a typical indirect association rule mining algorithm which can only mine size-2 indirect association rules between frequent items. IRRMiner is applied to make recommendations and shows that the identified implicit rules can increase recommendation reliability.

中文翻译：

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通过学习显式和隐藏项依赖来推断隐式规则

揭示实体之间的复杂关系（例如，交易内或交易之间的项目）对于业务优化、预测和决策制定具有重要意义。这种关系不仅包括基于共现的显式关系，还包括基于非共现的隐式关系。显式关系已经通过基于规则挖掘的方法进行了大量研究，包括关联规则挖掘和因果规则发现。相比之下，隐性关系受到的关注要少得多，但可能更具可操作性。在本文中，我们关注很少或从不共同出现的项目之间的隐式关系，而它们中的每一个都以很高的概率与其他相同的项目（链接项目）共同出现。一个框架集成了显式和隐藏项的依赖关系，相应的高效算法 IRRMiner 用隐式规则推理来捕获这种隐式关系。实验结果表明，IRRMiner 不仅能有效地推断出由频繁项和不频繁项组成的各种大小的隐式规则，而且运行速度至少比 IARMiner 快 4 倍，后者是典型的只能挖掘大小为 2 的间接关联规则的间接关联规则挖掘算法频繁项之间。应用 IRRMiner 进行推荐，并表明识别出的隐式规则可以提高推荐的可靠性。它的运行速度至少比 IARMiner 快 4 倍，这是一种典型的间接关联规则挖掘算法，只能挖掘频繁项之间的大小为 2 的间接关联规则。应用 IRRMiner 进行推荐，并表明识别出的隐式规则可以提高推荐的可靠性。它的运行速度至少比 IARMiner 快 4 倍，这是一种典型的间接关联规则挖掘算法，只能挖掘频繁项之间的大小为 2 的间接关联规则。应用 IRRMiner 进行推荐，并表明识别出的隐式规则可以提高推荐的可靠性。