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为准确预测煤与瓦斯突出,将主成分分析(PCA)法与聚类分析法相结合,建立了PCA-聚类分析模型对煤与瓦斯突出预测。利用主成分分析方法提取贡献率为原始变量85.28%的4个主成分代替原来的8个变量,并且计算了4个主成分的综合得分。将提取出的4个主成分作为系统聚类分析的输入变量,输入SPSS中得到谱系图。结果表明该模型能够预测煤与瓦斯突出,为煤与瓦斯突出预测提供了新方法。
In order to predict the coal and gas outburst accurately, PCA method and cluster analysis method are combined to establish PCA-cluster analysis model to predict coal and gas outburst. The principal component analysis method was used to extract four principal components with the contribution rate of 85.28% of the original variable instead of the original eight variables, and the comprehensive score of the four principal components was calculated. The extracted four principal components as input to the system cluster analysis input SPSS obtained pedigree map. The results show that this model can predict coal and gas outburst, which provides a new method for coal and gas outburst prediction.