Stability-based cluster analysis applied to microarray data

C. Giurcăneanu, I. Tabus, I. Shmulevich, Wei Zhang
{"title":"Stability-based cluster analysis applied to microarray data","authors":"C. Giurcăneanu, I. Tabus, I. Shmulevich, Wei Zhang","doi":"10.1109/ISSPA.2003.1224814","DOIUrl":null,"url":null,"abstract":"This paper studies the estimation of the number of clusters using the so-called stability-based approach, where clusters obtained for two subsets of the dataset are compared via a similarity index and the decision regarding the number of clusters is taken based on the statistics of the index over randomly selected subsets. We introduce a new similarity index s(/spl middot/,/spl middot/), and analyze the consistency of the estimator of the number of classes when k-means algorithm is used in conjunction with s(/spl middot/,/spl middot/). Various similarity indices are experimentally evaluated when comparing the \"true\" data partition with the partition obtained at each level of a hierarchical clustering tree. Finally, experimental results with real data are reported for a glioma microarray dataset.","PeriodicalId":264814,"journal":{"name":"Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings.","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSPA.2003.1224814","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11

Abstract

This paper studies the estimation of the number of clusters using the so-called stability-based approach, where clusters obtained for two subsets of the dataset are compared via a similarity index and the decision regarding the number of clusters is taken based on the statistics of the index over randomly selected subsets. We introduce a new similarity index s(/spl middot/,/spl middot/), and analyze the consistency of the estimator of the number of classes when k-means algorithm is used in conjunction with s(/spl middot/,/spl middot/). Various similarity indices are experimentally evaluated when comparing the "true" data partition with the partition obtained at each level of a hierarchical clustering tree. Finally, experimental results with real data are reported for a glioma microarray dataset.
基于稳定性的聚类分析应用于微阵列数据
本文使用所谓的基于稳定性的方法研究聚类数量的估计,其中通过相似性指数比较数据集的两个子集获得的聚类,并根据该指数对随机选择的子集的统计量来决定聚类数量。我们引入了一个新的相似度指标s(/spl middot/,/spl middot/),并分析了k-means算法与s(/spl middot/,/spl middot/)结合使用时类数估计量的一致性。当将“真实”数据分区与在分层聚类树的每个级别上获得的分区进行比较时,实验评估了各种相似性指标。最后,报告了胶质瘤微阵列数据集的实验结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
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