Many algorithms have proposed to solve the association rule learning problem. However, most of these algorithms suffer from the problem of scalability either because of tremendous time complexity or memory usage, especially when the dataset is large and the minimum support (minsup) is set to a lower number. This paper introduces a heuristic approach based on divide-and-conquer which may exponentially reduce both the time complexity and memory usage to obtain approximate results that are close to the accurate results. It is shown from comparative experiments that the proposed heuristic approach can achieve significant speedup over existing algorithms.

中文翻译：

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大型数据集的可扩展关联规则学习启发式

已经提出了许多算法来解决关联规则学习问题。然而，由于巨大的时间复杂度或内存使用，这些算法中的大多数都存在可扩展性问题，尤其是当数据集很大并且最小支持 ( minsup ) 设置为较低的数字时。本文介绍了一种基于分治法的启发式方法，该方法可以成倍地降低时间复杂度和内存使用量，以获得接近准确结果的近似结果。对比实验表明，与现有算法相比，所提出的启发式方法可以实现显着的加速。