Data analysis of print quality defect detection based on FP-Growth algorithm

Aiming at the problem of printing quality defect detection existing in traditional printing enterprises, this paper proposes a data mining and analysis method of printing factory quality defect detection based on FP-Growth algorithm, so as to solve the problem of correlation and analysis between the data of printing quality defect detection data informatization in printing enterprises. Firstly, the printing quality defect detection data acquisition system is set up. Then, the association rules between production data are obtained through the association analysis model of print quality defect detection based on FP-Growth algorithm. The experiment shows that this method is feasible in the analysis of print quality defect detection data. In this paper, the memory database storage method is introduced to optimize and upgrade the storage performance of FP-Growth algorithm, which improves the efficiency and accuracy of the algorithm.