基于差分演化的K-means算法在肝脏疾病中的应用

Application of K-MEANS Algorithm Based on Differential Evolution in Liver Disease

传统的K-均值算法依赖于初始聚类中心的选取,使聚类结果只能收敛于局部最优解;差分演化算法是一类利用随机偏差扰动产生新个体的方式获得非常好的收敛性的结果。为了克服K-均值聚类算法的上述缺点,该文提出基于差分演化的K-均值聚类算法,新算法结合K-均值算法的高效性和差分演化算法的全局优化能力,较好地解决了聚类中心优化问题。实验证明,此算法能够有效改善聚类质量。以肝功能疾病为例对新方法在医学中的应用进行了探讨。

The traditional K-means algorithm depends on the selection of the initial cluster centers,the clustering results can only converge to local optimal solution;Differential evolution algorithm is a class of random deviations disturbance to produce new in dividuals to obtain the very good convergence results.In order to overcome the shortcoming of K-means algorithm that men tion above,proposed a K-means clustering algorithm base on DE.The new algorithm proposed in this paper can well solve prob lem of optimizing cluster center by combining the high efficiency of K-means algorithm with the ability of global optimization of DE.The experimental results show that algorithm proposed in this paper has improved the clustering quality effectively.In this paper,it takes the Liver Disorders as example to discuss the new method of application in medicine.