湖北工程学院学报Issue(3)：36-39,4.
基于PAM和簇阈值的改进K-Means聚类算法
An Improved K-Means Algorithm Based on PAM Algorithm and Cluster Threshold
卜旭松 1刘立波 1石磊2
作者信息
- 1. 宁夏大学数学与计算机学院，宁夏银川750021
- 2. 湖北工程学院现代教育技术中心，湖北孝感432000
- 折叠
摘要
Abstract
In order to overcome the weakness of K‐Means algorithm which is sensitive to outliers ,and to improve the quality of K‐Means clustering algorithm ,the paper makes an in -depth study on the traditional K‐Means algorithm and proposes an improved clustering algorithm based on the PAM algo‐rithm and the cluster threshold .T he proposed method first samples the clustering data and then em‐ploys the PAM algorithm to obtain the clustering center of the sample data as the initial center of K‐Means algorithm .By calculating the threshold for each cluster dynamically in the iterative process of clustering ,the outliers can be excluded from the dataset .Experimental results indicate that the pro‐posed algorithm have been shown lower computational cost and higher clustering accuracy .关键词
采样/K-Means聚类/聚类阈值/孤立点Key words
sampling/K-Means cluster/cluster threshold/outlier分类
信息技术与安全科学引用本文复制引用
卜旭松,刘立波,石磊..基于PAM和簇阈值的改进K-Means聚类算法[J].湖北工程学院学报,2015,(3):36-39,4.
