An intelligent decision support system for agriculture economy based on fuzzy c-means

Abstract

Abstract Agricultural informatization construction has developed rapidly in recent years, but its data has not been well utilized to support decision-making. Cluster analysis is an important branch of data mining, which is applied to the agricultural industry and enhances the competitiveness of the industry. In response to the problem of insufficient information space services for rural agricultural economy, the application of clustering analysis algorithm in the field of agricultural feed industry was studied. Based on fuzzy C-means technology, clustering analysis was conducted, aiming to provide a universal decision support platform for agricultural production management departments and achieve value co-creation among farmers, enterprises, and investors. In this paper, a method based on Xie-Ben validity is adopted, and the method is modified. In addition, the steps of removing empty clusters are added after the cycle of FCM algorithm to improve the efficiency of the algorithm. In the experimental stage, different subdivision results are obtained by selecting different parameters, and the results are analyzed to verify the effectiveness of the FCM algorithm. Finally, the improved FCM algorithm is applied to the agricultural marketing decision support system to provide important decision-making for domestic and foreign feed raw material producers and traders, animal husbandry feed production enterprises, agricultural investment institutions and so on.