.png){kind=icon}
返回
A simple and fast algorithm for K-medoids clustering
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2008.01.039.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper proposes a new algorithm for K-medoids clustering which runs like the K-means algorithm and tests several methods for selecting initial medoids. The proposed algorithm calculates the distance matrix once and uses it for finding new medoids at every iterative step. To evaluate the proposed algorithm, we use some real and artificial data sets and compare with the results of other algorithms in terms of the adjusted Rand index. Experimental results show that the proposed algorithm takes a significantly reduced time ill computation with comparable performance against the partitioning around medoids. (C) 2008 Elsevier Ltd. All rights reserved.
Keyword:
Clustering
K-means
K-medoids
Rand index
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
机构
暂无机构信息
引用论文
Exotic- and native-dominated shrubland habitat use by fall migrating Swainson's Thrushes and Gray Catbirds in Michigan, USA.png)
The Condor
The CondorIF0
PREAI
分享收藏A Concise Catalytic Route to the Marine Sesquiterpenoids (–)‐Clavukerin A and (–)‐Isoclavukerin A
PREAI
分享收藏Empirical comparison of fast partitioning-based clustering algorithms for large data sets基于快速划分的大数据集聚类算法的实证比较
PREAI
分享收藏分享收藏 没有更多内容
没有更多内容