Parallel computing is an effective method to solve computationally large and data‐intensive problems. The traditional data mining algorithm cannot mining association rules for large amounts of streaming data in a timely and effectively. In order to improve the speed and accuracy of association rules mining, distributed and parallel algorithms have become a research focus. This paper proposes a parallel FP‐growth approach using Spark Streaming, called SSPFP, which can parallel mining frequent itemsets and association rules in real‐time streaming data. In this paper, the proposed SSPFP algorithm is applied to mining the association rules between temperature and salinity in marine Argo data. The experimental results indicate that SSPFP algorithm is efficient for association rules mining.

中文翻译：

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一种使用Spark Streaming并行挖掘关联规则的改进方法

并行计算是解决计算量大且数据密集型问题的有效方法。传统的数据挖掘算法无法及时有效地挖掘大量流数据的关联规则。为了提高关联规则挖掘的速度和准确性，分布式和并行算法已成为研究的热点。本文提出了一种使用Spark Streaming的并行FP增长方法，称为SSPFP，该方法可以并行挖掘实时流数据中的频繁项集和关联规则。本文将提出的SSPFP算法用于挖掘海洋Argo数据中温度与盐度的关联规则。实验结果表明，SSPFP算法对于关联规则挖掘是有效的。