计算机技术与发展Issue(10)：30-33,4.DOI:10.3969/j.issn.1673-629X.2014.10.007
一种改进的K-Means算法
An Improved K-Means Clustering Algorithm
摘要
Abstract
Aiming at the problemsof too much iterative times in selecting initial centroids stochastically for K-Means algorithm,a method is proposed to optimize the initial centroids through cutting the set into k segmentations and select one point in each segmentation as initial centroids for iterative computing. A new valid function called clustering-index is defined as the sum of clustering-density and clustering-significance and can be used to search the optimization of k in the internal of [1, n ]. The simulation experiment with IRIS data set shows that the proposed algorithm converges faster and the value k found is close to the actual value,which proves the validity of the al-gorithm.关键词
K-Means算法/分段/聚类指数/紧密度/显著度Key words
K-Means algorithm/segmentation/clustering-index/density/significance分类
信息技术与安全科学引用本文复制引用
尹成祥,张宏军,张睿,綦秀利,王彬..一种改进的K-Means算法[J].计算机技术与发展,2014,(10):30-33,4.基金项目
国家自然科学基金资助项目(70971137) （70971137）
