云南民族大学学报（自然科学版）2019，Vol.28Issue(2)：160-164,5.DOI:10.3969/j.issn.1672-8513.2019.02.013
基于密度的全局K-means算法的改进
Improvement of the global K-means algorithm based on density
摘要
Abstract
When selecting the cluster center point of the next cluster, it is necessary for the global k-means clustering algorithm and the fast global k-means clustering algorithm to calculate the intra-cluster square error when each point in the dataset is used as the candidate cluster center point one by one. However, there are many noise points in the data set, and it is not possible to use them candidate points. In order to eliminate noise points, a high-density-based DGK-means algorithm is proposed and tested by four sets of data sets in the UCI database. The comparison of the improved DGK-means algorithm with the global?k-means clustering algorithm and the fast global k-means clustering algorithm reveals that under the premise of the stable clustering effect, the clustering time of improved algorithm is shorter and its efficiency is better.关键词
GK-means算法/FGK-means算法/DGK-means算法/高密度数Key words
GK-means algorithm/FGK-means algorithm/DGK-means algorithm/high-density number分类
信息技术与安全科学引用本文复制引用
徐娟,范菁,陈楚天,曲金帅..基于密度的全局K-means算法的改进[J].云南民族大学学报（自然科学版）,2019,28(2):160-164,5.基金项目
国家自然科学基金(61540063) （61540063）
云南省应用基础研究计划项目(2018FD055) （2018FD055）
云南省教育厅科学研究基金(2017ZDX045) （2017ZDX045）
云南民族大学校级科研项目(2017QN02) （2017QN02）
云南省高校科技创新团队开放式基金 （）
