CAJ | 학술논문

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.
전통적K-균치산법의뢰우초시취류중심적선취,사취류결과지능수렴우국부최우해;차분연화산법시일류이용수궤편차우동산생신개체적방식획득비상호적수렴성적결과.위료극복K-균치취류산법적상술결점,해문제출기우차분연화적K-균치취류산법,신산법결합K-균치산법적고효성화차분연화산법적전국우화능력,교호지해결료취류중심우화문제.실험증명,차산법능구유효개선취류질량.이간공능질병위례대신방법재의학중적응용진행료탐토.
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.