Data Analysis of Tyre Quality Based on Improved FP-Growth Algorithm

被引：5
|
作者：
Li M. [1 ,2 ]
Ding D. [1 ]
Yi Y. [1 ]
机构：
[1] Software School, Fudan University, Shanghai
[2] Shanghai Key Laboratory of Data Science, Fudan University, Shanghai
来源：
Zhongguo Jixie Gongcheng/China Mechanical Engineering | 2019年 / 30卷 / 02期
关键词：
Data mining; FP-growth algorithm; Industrial big data; Quality analysis;
D O I：
10.3969/j.issn.1004-132X.2019.02.017
中图分类号：
学科分类号：
摘要：
According to the problem analyses of abnormal quality in tyre manufacturing processes, tyre quality data acquisition, effective integration and data analysis processes were discussed. The structured data sets associated with production data and product inspection data were constructed based on Hive data warehouse. For the existing frequent pattern-growth (FP-Growth) algorithm, the performance of FP-tree was low, an improved FP-growth algorithm was proposed. A new tail attribute was added to the existing header table of frequent item and accelerate the construction of FP-tree. The experiments show that the improved FP-growth algorithm may effectively improve the correlation analysis efficiency of tyre quality abnormal data. The improved FP-growth algorithm is able to identify the factors that affect the quality of tire productions, and it is also suitable for large data mining. © 2019, China Mechanical Engineering Magazine Office. All right reserved.
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页码：244 / 251
页数：7
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