Design and Implementation of Automobile Industry Cost Management System Based on Data Mining and Fuzzy Clustering Algorithm

There are many and miscellaneous quality data, and it is extremely difficult to collect data. In recent years, in the world automobile market, the fierce competition of automobile giants is concentrated on cost reduction. Modern cost management starts from the production and sales of automobile manufacturing enterprises, decomposes and traces the expenses incurred, analyzes the input-output ratio of funds in each stage, and re-optimizes the cost management measures of enterprises according to market conditions. In this paper, the design of cost management system of automobile industry based on DM (data mining) and fuzzy clustering algorithm is launched. With ERP as the means, the operation platform is formed according to the enterprise value chain, business chain and activity chain; Build a data processing platform based on basic theories such as cost management theory, integration theory and strategic cost theory; The information platform of the system is generated through the processing of the operation platform and the data platform. The FCM (Fuzzy c-means) clustering algorithm is improved by using particle swarm chaos differential training to optimize DM model. The research results show that the efficiency of database query of this algorithm is improved by 9.035% compared with the traditional algorithm. The algorithm in this paper can improve the query efficiency of database query, showing superior performance.