The Optimization of Parallel Frequent Pattern Growth Algorithm Based on Mahout in Cloud Manufacturing Environment

In cloud manufacturing environment, many manufacturing enterprises will produce massive data of a variety of forms. We do research of optimization parallel frequent pattern mining algorithm based on Mahout in this paper. We first analyze the implement and defects of PFP-Growth in Mahout. Then we propose two optimization strategies. One is parallel sequence optimization, and another is optimization the storage of counting information. Datasets from real manufacturing and Webdocs show the effectiveness of the strategy in time and space of the optimization.