西安工程大学学报2016，Vol.30Issue(3)：388-392,5.DOI:10.13338/j.issn.1674-649x.2016.03.020
基于扰动因子的相似度下的聚类算法
Clustering algorithm under the similarity based on the disturbance factor
摘要
Abstract
The clustering centers are very sensitive to outliers and easy to fall into local mini-mum when they are calculated by the classical K-means clustering algorithm.Aimed at the dis-advantage,the clustering algorithm under the similarity based on the disturbance factor is es-tablished by the distance method to eliminate outliers from the cluster center for influence,and to add a set of disturbance factors which decrease with the number of iterations to searching space.Finally,the improved algorithm is compared to the classical K-means clustering algo-rithm by the experiments.The results show that the improved algorithm is more stable than before,and the clustering effect is better.关键词
K-均值聚类算法/离群点/聚类中心/扰动因子Key words
K-means clustering algorithm/outlier/clustering center/disturbance factor分类
信息技术与安全科学引用本文复制引用
满扬,王晓东..基于扰动因子的相似度下的聚类算法[J].西安工程大学学报,2016,30(3):388-392,5.基金项目
陕西省自然科学基金资助项目(2015JM1012) （2015JM1012）
