Association Analysis of Complex Product Digital Prototype Data Based on the Improved FP-Growth Algorithm

The data of complex product digital prototype with complex relationships have massive, high-dimensional features. Its relevance mining and performance evaluation is a difficult problem. Data mining algorithms can be used to solve the data feature association problem of complex performance. This paper presents an improved FP-Growth algorithm. Improved algorithm adds the weight setting of each dimension attribute to avoid generating redundant false rules due to the uneven distribution of attributes. It constructs the function to map the attribute name and the weighted support count, and reduces the number of times of traversing the frequent item list. It divides the subset of each dimension attribute and constructed the conditional pattern tree of each dimension subset to improve the efficiency of the original algorithm. Based on the improved FP-Growth algorithm, taking the digital prototype data of the bag filter as an example, the parameter combination model was analyzed from the multidimensional angle. It generated the best parameter combination association rule of the bag filter wind performance. It is proved that the improved FP-Growth algorithm is effective and efficient.