K-means初始聚类中心的选择算法  

Selection algorithm for K-means initial clustering center

作　　者：郑丹[1,2] 王潜平[2] 

机构地区：[1]江苏师范大学人事处,江苏徐州221116 [2]中国矿业大学计算机科学与技术学院,江苏徐州221116

出　　处：《计算机应用》2012年第8期2186-2188,2192,共4页journal of Computer Applications

基　　金：国家科技支撑计划项目(2008BAH37B05095)

摘　　要：K-means算法随机选取初始聚类中心,容易造成聚类准确率低且聚类结果不稳定。针对这一问题,提出一种初始聚类中心的选择算法。通过k-dist的差值(DK)图分析,确定数据点在k-dist图上的位置,选择主要密度水平曲线上k-dist值最小的点作为初始聚类中心。实验证明,改进算法选择的初始聚类中心唯一,聚类结果稳定,聚类准确率高,迭代次数少。The initial clustering centers of K-means algorithm are randomly selected, which may result in low accuracy and unstable clustering. To solve these problems, a K-means initial clustering center selection algorithm was proposed. The locations of data points were determined by analyzing Difference of K-dist （DK） graph. One point with the least k-dist value on the main density curves was selected as an initial clustering center. The experimental results demonstrate that the improved algorithm can select unique initial clustering center, gain stable clustering result, get higher accuracy and reduce times of iteration.

关 键 词：聚类 K-MEANS算法 k-dist图 k-dist的差值图 密度 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]