摘要: 传统的k-means算法要求用户事先给定k值,限制了很多应用,初始中心点随机选择,容易导致局部极值点,常用的评价函数对于求解最优聚类数目也不理想。针对这些问题,该文提出一种新的评价函数——均衡化函数,采用基于密度的初始化中心点选择算法,自动生成聚类数目,实验结果表明了改进算法的有效性。
关键词:
k-均值算法,
密度,
初始中心点,
均衡化函数
Abstract: In traditional k-means algorithm, value k must be confirmed in advance, which restricts a large number of practical applications. Initial centers are selected randomly so that local extremums will be introduced. The common evaluate functions to the optimum number of clustering can not be effectively calculated. To conquer these problems, a new evaluation function-equalization function based on the density of the center initialization algorithms introduced, and the number of generation clustering are automatically calculated. Experimental results prove the efficiency of the improved k-means algorithm.
Key words:
k-means algorithm,
density,
initial center point,
balanced function
中图分类号:
钱雪忠;施培蓓;张明阳;汪 中. 基于均衡化函数的k均值优化算法[J]. 计算机工程, 2008, 34(14): 60-62.
QIAN Xue-zhong; SHI Pei-bei; ZHANG Ming-yang; WANG Zhong. Optimized k-means Algorithm Based on Balanced Function[J]. Computer Engineering, 2008, 34(14): 60-62.