基于改进K均值算法的医保就医行为研究

Research on medical insurance behavior based on improved k-means algorithm

传统K均值算法聚类分析结果受到聚类数目和聚类中心的影响比较大,迭代收敛速度慢,易陷入局部最优。本文对聚类数目和聚类中心进行优化,得到改进的K均值聚类算法,并应用于医保就医行为分析中,同时和CBM挖掘算法进行对比。结果表明,改进K均值聚类算法对医保就医行为聚类分析结果精度高,算法性能良好,比CBM算法具有更高的运行效率。聚类分析结果为特定的就医人群提供专业化服务,这对提升医保就医人群满意度具有一定的参考价值。

The traditional K-means clustering analysis results are greatly affected by the number of clusters and cluster centers,and the iterative convergence speed is slow,so it is easy to fall into local optimum.In this paper,we optimize the number and center of clustering,get the improved k-means clustering algorithm,and apply it to the analysis of medical insurance behavior,and compare it with the CBM mining algorithm.The results show that the improved k-means clustering algorithm has higher accuracy and better performance than CBM algorithm.The results of cluster analysis provide professional services for specific medical population,which has a certain reference value for improving the satisfaction of medical insurance population.