Outlier detection model modified based on MOPSO algorithm
Mining the implied value of industrial big data is an important research direction of intelligent manufactur-ing,and carrying out outlier detection is a prerequisite for realizing data analysis.The main problems addressed by industrial big data anomaly detection were introduced,and the relevant definitions in this paper were proposed.Based on the Multi-Objective Particle Swarm Optimization algorithm(MOPSO),an improved DBSCAN model for industrial big data outlier detection was proposed.The algorithm design idea and algorithm steps of the model were introduced,the pseudo-code of the algorithm was completed,and the calculation the time complexity of the algo-rithm was proposed.The model simulation and experiments were carried out by using the manufacturing data of an electric core factory,and it was verified that the proposed model could improve the accuracy of industrial big data outlier detection.This paper provided a reference for the application of data mining in industrial outlier detection.
industrial big dataoutlier detectionmulti-objective particle swarm optimization algorithmDBSCAN model
万方数据
维普
