The invention belongs to the technical field of industrial
big data, and particularly relates to an improved product quality abnormal data FP-Growth
correlation analysis method. The main content of the method of the invention comprises the product quality abnormal
data association analysis characterized in that the multi-factor association analysis is carried out on the product production qualityand the production process data, a series of association rules are mined based on a multi-factor association analysis
algorithm, the potential problems of some indexes in the
quality data are found, and the factors causing product quality abnormal influences are conveniently positioned; the FP-
Tree data structure improvement characterized in that a field
tail _ Link is newly added on the basis ofan FP-Tree frequent item header table, and the current last node of each data item is recorded, so that the repeated traversal
linked list operation when a new node is inserted is avoided, and the FP-Tree tree building efficiency is improved; the improvement of the parallelization strategy of the FP-Growth association
algorithm characterized in that an FP-Growth
algorithm mining frequent mode is executed parallelly on each transaction set group to break through the original mode of firstly building a tree and then parallelizing, so that the calculation efficiency of each node in the parallelization execution is improved.