Accelerated algorithm for intelligent mining of communication data in cloud computing environment

Issue title: Big Data Networking-Challenges and Applications

Guest editors: Zhixiang Hou and Chong Shen

Article type: Research Article

Authors: Jiang, Xiao-Ling^{; *} | Zhang, Hui

Affiliations: Department of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian 223001, China

Correspondence: [*] Corresponding author. E-mail: [email protected].

Abstract: Traditional methods have some problems such as large memory occupation and slow mining speed, so an intelligent mining acceleration algorithm based on particle swarm optimization is proposed. Based on the analysis of the communication data, the features of the communication data are selected by the acceleration strategy, and multiple feature subsets of the communication data are obtained repeatedly by using the remaining attributes. Particle swarm optimization (pso) algorithm is used to select the optimal feature subset, and average classification error is used as fitness function to complete intelligent mining of communication data. The experimental results show that the memory usage of this algorithm is between 62 and 71 GB in the experimental process, which is small and the average running time is better than the traditional algorithm. The results show that the algorithm has lower memory consumption and faster mining speed.

Keywords: Cloud computing, communication data, mining, particle swarm optimization

DOI: 10.3233/WEB-200436

Journal: Web Intelligence, vol. 18, no. 2, pp. 145-153, 2020