基于改进的自适应传播模型的农业风险区划分析

摘要

Variables for individuals are developed with dynamic characteristics in many panel data sets when we deal with agriculture insurance pricing . In papers for panel data clustering , the similarity coefficients are computed by the numerical character ,distribution character ,and fluctuant character ,but the clustering results cannot reflect the dynamic characteristics .This paper proposes the method to apply adaptive affinity propagation clustering (ad‐AP) ,which is improved from affinity propagation clustering , to optimize panel data set ,and compute the best exemplars of each individual w hich constitute a new data set .Then panel data clustering analysis is transform into the new dataset clustering analysis .Experimental results on china agriculture insurance show the validity ,practicability and interpretability of the design for panel data with dynamic characteristics .%农业险定价中的核心问题是农业风险区划问题，为了体现农业区划中个体指标的动态发展特征，根据近邻传播改进自适应近邻传播聚类方法对数据进行优化，基于轮廓系数、归属度和吸引度得到最佳聚类中心和几何聚类中心，并将聚类转化为新数据集的聚类问题；选取代表性的棉花为例进行实证分析，通过计算生产、销售、收入、财政等指标进行棉花风险区划实例分析，计算最优棉花风险区划，结果表明对于具有动态特征的数据，本模型具有很好的有效性、实用性和解释性。