An improved approach for mining association rules in parallel using Spark Streaming
Longtao Liu, Jiabao Wen, Zexun Zheng, Hansong Su
Abstract
Summary 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.