华东理工大学学报（自然科学版）2017，Vol.43Issue(6)：849-854,862,7.DOI:10.14135/j.cnki.1006-3080.2017.06.015
xk-split:基于k-medoids的分裂式聚类算法
xk-split:A Split Clustering Algorithm Bases on k-medoids
陈逸斐 1虞慧群2
作者信息
- 1. 华东理工大学计算机科学与工程系,上海200237
- 2. 上海市计算机软件重点测评实验室,上海201112
- 折叠
摘要
Abstract
In recent years,the scale of internet data has explosive growth,which makes big data analysis become a hot topic.However,it is difficult to directly utilize the collected data,so a certain degree of pretreatment had to be made in order to improve the quality of big data.In this work,the data set will be gradually divided into smaller subsets by using the split iterative process,which can effectively avoid the limitation of traditional clustering algorithm and reduce the time complexity.In addition,by thresholdbased noise data filtering,the dirty data can be eliminated during the iterative process so as to enhance the tolerance of the clustering algorithm to the dirty data.关键词
数据挖掘/聚类/k-means/k-medoids/分裂Key words
data mining/clustering/k-means/k-medoids/split分类
信息技术与安全科学引用本文复制引用
陈逸斐,虞慧群..xk-split:基于k-medoids的分裂式聚类算法[J].华东理工大学学报（自然科学版）,2017,43(6):849-854,862,7.
