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.