A novel k-means clustering algorithm and its application

首页 > 成果 > 详情

认领

导出

Link by DOI

反馈

作者信息关键词期刊信息基础信息归属信息摘要

成果类型：

期刊论文

作者：

Yingying Peng;Kenli Li;Man Li

通讯作者：

Peng, Yingying

作者机构：

[Li M.] College of Management and Information Engineering, Hunan University of Chinese Medicine, Changsha, Hunan, 410208, China

[Li K.] College of Information Science and Engineering, Hunan University, Changsha, Hunan, 410082, China

[Peng Y.] College of Management and Information Engineering, Hunan University of Chinese Medicine, Changsha, Hunan, 410208, China<&wdkj&>College of Information Science and Engineering, Hunan University, Changsha, Hunan, 410082, China

通讯机构：

College of Management and Information Engineering, Hunan University of Chinese Medicine, Changsha, Hunan, China

语种：

英文

关键词：

Algorithms;Data mining;Cluster;Clustering results;ITS applications;K-means;k-Means algorithm;K-Means clustering algorithm;k-NN algorithm;Spherical shape;Clustering algorithms

期刊：

Journal of Computational and Theoretical Nanoscience

ISSN：

1546-1955

年：

2015

卷：

期：

页码：

3658-3661

DOI：

10.1166/jctn.2015.4253

机构署名：

本校为第一且通讯机构

院系归属：

信息科学与工程学院

摘要：

K-Means algorithm has been researched adequately in recent years. Clustering result of traditional K-Means algorithm is affected by the choice of initial point and noise. In addition to, traditional K-Means algorithm only favors clusters with spherical shapes and similar sizes. A novel K-Means algorithm combining K-Means algorithm and KNN algorithm called KK -Means is proposed to solve these weaknesses in this paper. Experimental result shows that KK -Means algorithm has better performance more than traditional K-Means ...

反馈

产权有误：本人成果被他人认领

数据有误：数据基本信息有误

归属有误：成果的院系归属、机构署名归属有误

其他原因：

验证码：

看不清楚，换一个

确定

取消

成果认领

标题：

用户	作者	通讯作者	--
	请选择	请选择	--

确定

取消

A novel k-means clustering algorithm and its application

反馈

成果认领

提示

该栏目需要登录且有访问权限才可以访问