The high computational impact when mining fuzzy association rules grows significantly when managing very large data sets, triggering in many cases a memory overflow error and leading to the experiment failure without its conclusion. It is in these cases when the application of Big Data techniques can help to achieve the experiment completion. Therefore, in this paper several Spark algorithms are proposed to handle with massive fuzzy data and discover interesting association rules. For that, we based on a decomposition of interestingness measures in terms of α-cuts, and we experimentally demonstrate that it is sufficient to consider only 10 equidistributed α-cuts in order to mine all significant fuzzy association rules. Additionally, all the proposals are compared and analysed in terms of efficiency and speed up, in several datasets, including a real dataset comprised of sensor measurements from an office building.

在管理非常大的数据集时，挖掘模糊关联规则时的高计算影响显着增加，在许多情况下触发内存溢出错误并导致实验失败而没有结论。正是在这些情况下，大数据技术的应用可以帮助实现实验的完成。因此，本文提出了几种 Spark 算法来处理海量模糊数据并发现有趣的关联规则。为此，我们基于对α -cuts的兴趣度量的分解，并且我们通过实验证明仅考虑 10 个等分布的α就足够了-cuts 以挖掘所有重要的模糊关联规则。此外，所有提案都在几个数据集中在效率和加速方面进行了比较和分析，包括一个由办公楼传感器测量组成的真实数据集。