实现概率密度函数聚类的非分层算法的R代码

Ngoc Diem Tran, Tom Vinant, ThéO Marc Colombani, Kieu Diem Ho
{"title":"实现概率密度函数聚类的非分层算法的R代码","authors":"Ngoc Diem Tran, Tom Vinant, ThéO Marc Colombani, Kieu Diem Ho","doi":"10.25073/JAEC.201823.194","DOIUrl":null,"url":null,"abstract":"This paper aims to present a code for implementation of non-hierarchical algorithm to cluster probability density functions in one dimension for the first time in R environment. The structure of code consists of 2 primary steps: executing the main clustering algorithm and evaluating the clustering quality. The code is validated on one simulated data set and two applications. The numerical results obtained are highly compatible with that on MATLAB software regarding computational time. Notably, the code mainly serves for educational purpose and desires to extend the availability of algorithm in several environments so as having multiple choices for whom interested in clustering.  This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.","PeriodicalId":250655,"journal":{"name":"J. Adv. Eng. Comput.","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"An R code for implementing non-hierarchical algorithm for clustering of probability density functions\",\"authors\":\"Ngoc Diem Tran, Tom Vinant, ThéO Marc Colombani, Kieu Diem Ho\",\"doi\":\"10.25073/JAEC.201823.194\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper aims to present a code for implementation of non-hierarchical algorithm to cluster probability density functions in one dimension for the first time in R environment. The structure of code consists of 2 primary steps: executing the main clustering algorithm and evaluating the clustering quality. The code is validated on one simulated data set and two applications. The numerical results obtained are highly compatible with that on MATLAB software regarding computational time. Notably, the code mainly serves for educational purpose and desires to extend the availability of algorithm in several environments so as having multiple choices for whom interested in clustering.  This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.\",\"PeriodicalId\":250655,\"journal\":{\"name\":\"J. Adv. Eng. Comput.\",\"volume\":\"8 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-09-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"J. Adv. Eng. Comput.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.25073/JAEC.201823.194\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"J. Adv. Eng. Comput.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.25073/JAEC.201823.194","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

本文旨在首次在R环境下实现一维概率密度函数聚类的非分层算法代码。代码结构包括两个主要步骤:执行主聚类算法和评估聚类质量。该代码在一个模拟数据集和两个应用程序上进行了验证。所得到的数值结果在计算时间上与MATLAB软件的结果高度吻合。值得注意的是,该代码主要用于教育目的,并希望扩展算法在多个环境中的可用性,以便为对聚类感兴趣的人提供多种选择。这是一篇在知识共享署名许可(http://creativecommons.org/licenses/by/4.0/)下发布的开放获取文章,该许可允许在任何媒体上不受限制地使用、分发和复制,前提是正确引用原始作品。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An R code for implementing non-hierarchical algorithm for clustering of probability density functions
This paper aims to present a code for implementation of non-hierarchical algorithm to cluster probability density functions in one dimension for the first time in R environment. The structure of code consists of 2 primary steps: executing the main clustering algorithm and evaluating the clustering quality. The code is validated on one simulated data set and two applications. The numerical results obtained are highly compatible with that on MATLAB software regarding computational time. Notably, the code mainly serves for educational purpose and desires to extend the availability of algorithm in several environments so as having multiple choices for whom interested in clustering.  This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信