Research on the Development of Modern Design Through Data Mining Technology

Abstract

Logistics operations heavily rely on efficient route planning and optimization to ensure the smooth flow of goods and services. To enhance these processes, the simulation of logistics frequent path data mining based on statistical density offers valuable insights. By analyzing vast amounts of historical transportation data, statistical density-based methods can identify frequent paths and patterns in logistics networks. With the data mining process, the statistical density of the logistics in China is computed. The model uses the Fuzzy Associative Monte Carlo (FAMC). The proposed FAMC model estimates the associative rules in the fuzzy model for the computation of the frequent pattern for the estimation of the logistics in the data mining process. Through FAMC model statistical density is computed with an estimation of the logistics path to compute the statistical density model. The logistics path routes are estimated based on the computation of the statistical density for the computation of the data mining-based approach in logistics management. The proposed FAMC model effectively computes the path in the logistics in China with a significant density analysis in a statistical manner.