Trusted Network Difference Data Mining Algorithm Based on Artificial Bee Colony Optimization

Abstract

Trusted network is characterized by a large amount of data, abnormal dispersion, and high complexity. Traditional methods are easily affected by trusted network environment, resulting in unreliable mining results. Therefore, a new real-time mining method of trusted network difference data is proposed. Real-time collection of trusted network difference data through history system is performed on the basis of determining the principle of trusted network difference data mining and collecting and extracting the characteristics of difference data. The process of trusted network differential data mining is designed through the artificial bee colony algorithm. According to the process, differential data mining is carried out from three aspects: constructing a trusted network differential data transmission path, updating pheromone, and establishing a differential data transmission path set. The experimental results show that the proposed method can effectively realize the real-time mining of difference data, and the mining accuracy is more accurate.