Computer-Aided Data Analysis of Clothing Pattern Based on Popular Factors

With the increasing diversification of clothing patterns and the increasing amount of pattern data, traditional data analysis methods can no longer meet the current needs for clothing pattern analysis. In order to comprehensively analyze the clothing version style data, a clustering algorithm based on popular factors is used to analyze it. By classifying the clothing version style data, the clothing style characteristics of each category are analyzed, and the differences between the various versions are found. Then, the popularity of clothing styles can be predicted by applying decision tree algorithm, and the use of computational auxiliary data analysis can provide decision-making reference for textile and clothing enterprises' production, and the accuracy can be increased by 7.91%.