Self-organizing data mining research on the decision support system for environmental evolution analysis
Purpose - The purpose of this paper is to select the main impact factors of environment change automatically and identify and analyze the potential environmental impact factors with time delay by computer simulation, analyzing the impact rate of environmental impact factors. Then, the environmental impact factors analysis decision support system based on self-organizing data mining model is designed Design/methodology/approach - Applying data mining methods in the analysis and decision making of regional environmental impact factors will have broad perspective. Self-organization data mining is anew modeling method of complex systems modeling with strong modeling capability It was first presented by A.G Ivakhnenko, based on the principle of self-organization of biological cybernetics and Kolmogoorov-Gabor polynomial function In this paper, the impact factors of regional environment quality evolution based on self-organization data mining method is studied, selecting the main impact factors of environment change automatically by computer simulation, analyzing the impact contribution rate of environmental impact factors. Findings - The environmental impact factors analysis decision support system based on self-organizing data mining model is designed. Research limitations/implications - Accessibility and availability of data are the main limitations affecting which model will be applied Practical implications - The paper has important theoretical and practical significance for the sustainable development of regional environment, resource. economy system and the constitution of environmental protection and management measures. Originality/value - This paper not only exploits new application domains of self-organization data mining, but also explores new ways for regional environment impact factors analysis
decision support systems simulation cybernetics data collection environmental management