Big Data Clustering Optimization Based on Intuitionistic Fuzzy Set Distance and Particle Swarm Optimization for Wireless Sensor Networks

Abstract

Big data clustering plays an important role in the field of data processing in wireless sensor networks. However, there are some problems such as poor clustering effect and low Jaccard coefficient. This paper proposes a novel big data clustering optimization method based on intuitionistic fuzzy set distance and particle swarm optimization for wireless sensor networks. This method combines principal component analysis method and information entropy dimensionality reduction to process big data and reduce the time required for data clustering. A new distance measurement method of intuitionistic fuzzy sets is defined, which not only considers membership and non-membership information, but also considers the allocation of hesitancy to membership and non-membership, thereby indirectly introducing hesitancy into intuitionistic fuzzy set distance. The intuitionistic fuzzy kernel clustering algorithm is used to cluster big data, and particle swarm optimization is introduced to optimize the intuitionistic fuzzy kernel clustering method. The optimized algorithm is used to obtain the optimization results of wireless sensor network big data clustering, and the big data clustering is realized. Simulation results show that the proposed method has good clustering effect by comparing with other state-of-the-art clustering methods.