A novel k-means clustering algorithm and its application

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成果类型：

期刊论文

作者：

Peng, Yingying*;Li, Kenli;Li, Man

通讯作者：

Peng, Yingying

作者机构：

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

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

[Peng, Yingying] 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 ...

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A novel k-means clustering algorithm and its application

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