{"title":"利用独立集寻找基因共表达网络中的簇数","authors":"Harun Pirim","doi":"10.1109/SocialCom.2013.125","DOIUrl":null,"url":null,"abstract":"Determining the number of clusters is required for most of the clustering algorithms. The number of clusters in a gene co-expression network is not known a prior. In this study, maximum independent set concept from graph theory is applied for a gene expression data set. The results indicate that employing independent set approach to approximate the number of clusters is promising.","PeriodicalId":129308,"journal":{"name":"2013 International Conference on Social Computing","volume":"86 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Finding Number of Clusters in a Gene Co-expression Network Using Independent Sets\",\"authors\":\"Harun Pirim\",\"doi\":\"10.1109/SocialCom.2013.125\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Determining the number of clusters is required for most of the clustering algorithms. The number of clusters in a gene co-expression network is not known a prior. In this study, maximum independent set concept from graph theory is applied for a gene expression data set. The results indicate that employing independent set approach to approximate the number of clusters is promising.\",\"PeriodicalId\":129308,\"journal\":{\"name\":\"2013 International Conference on Social Computing\",\"volume\":\"86 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-09-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 International Conference on Social Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SocialCom.2013.125\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 International Conference on Social Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SocialCom.2013.125","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Finding Number of Clusters in a Gene Co-expression Network Using Independent Sets
Determining the number of clusters is required for most of the clustering algorithms. The number of clusters in a gene co-expression network is not known a prior. In this study, maximum independent set concept from graph theory is applied for a gene expression data set. The results indicate that employing independent set approach to approximate the number of clusters is promising.