与计算机密度相关的指标,基于密度期望和有效性指标的K-均值算法-计算机工程与应用.PDF...
基于密度期望和有效性指标的K-均值算法-计算机工程与应用
Computer Engineering and Applications 计算机工程与应用 2013 ，49 (24 ) 105
⦾数据库、数据挖掘、机器学习⦾
基于密度期望和有效性指标的K-均值算法
何云斌，肖宇鹏，万 静，李 松
HE Yunbin, XIAO Yupeng, WAN Jing, LI Song
哈尔滨理工大学 计算机科学与技术学院，哈尔滨 150080
School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China
HE Yunbin, XIAO Yupeng, WAN Jing, et al. Improved K-means algorithm based on expectation of density and clustering
validity index. Computer Engineering and Applications, 2013, 49 (24 )：105-111.
Abstract ：The traditional K -means clustering algorithm must be given in advance the number of clusters k, but in the actual cases,
k is difficult to establish; in addition, traditional K -means clustering algorithm is sensitive to initialization and easily falls into
local optimum. In view of this, this paper presents an improved K -means algorithm based on expectation of density and Silhou-
ette validity index. The algorithm chooses the furthest mutual distance k sample objects as the initial centers, which belong to the
expectation of density region. The experimental result shows that the improved K -means algorithm has not only the weak depen-
dence on initial data, but also fast convergence and high clustering quality. Meanwhile, the new algorithm can automatically
analyze the clustering quality in different k values and determine the optimal number of clusters by selecting the Silhouette validity
index. The experiment and analysis demonstrate the feasibility and effectiveness of the proposed algorithm.
Key words ：K -means clustering; initial clustering centers; expectation of density; optimization of k
摘 要：传统K -均值聚类算法虽然收敛速度快，但存在聚类数k 无法预先确定，并且算法对初始中心点敏感的缺点。针对上
述缺点，提出了基于密度期望和聚类有效性Silhouette 指标的K -均值优化算法。给出了基于密度期望的初始中心点选取方
案，将处于密度期望区间内相距最远的k 个样本作为初始聚类中心。该方案可有效降低K -均值算法对初始中心点的依赖，从
而获得较高的聚类质量。在此基础上，可进一步通过选择合适的聚类有效性指标Silhouette 指标分析不同k 值下的每次聚类
结果，确定最佳聚类数，则可有效改善k 值无法预先确定的缺点。实验及分析结果验证了所提出方案的可行性和有效性。
关键词：K -均值聚类；初始聚类中心点；期望密度；k