{"title":"数据中聚类数的双准则确定","authors":"Kaixun Hua, D. Simovici","doi":"10.1109/SYNASC.2018.00040","DOIUrl":null,"url":null,"abstract":"We present a method for determining the number of clusters existent in a data set involving a bi-criteria optimization that makes use of the entropy and the cohesion of a partition. The results are promising and may be applicable in dealing with clusterings of imbalanced data.","PeriodicalId":273805,"journal":{"name":"2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Dual Criteria Determination of the Number of Clusters in Data\",\"authors\":\"Kaixun Hua, D. Simovici\",\"doi\":\"10.1109/SYNASC.2018.00040\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a method for determining the number of clusters existent in a data set involving a bi-criteria optimization that makes use of the entropy and the cohesion of a partition. The results are promising and may be applicable in dealing with clusterings of imbalanced data.\",\"PeriodicalId\":273805,\"journal\":{\"name\":\"2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)\",\"volume\":\"41 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SYNASC.2018.00040\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SYNASC.2018.00040","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Dual Criteria Determination of the Number of Clusters in Data
We present a method for determining the number of clusters existent in a data set involving a bi-criteria optimization that makes use of the entropy and the cohesion of a partition. The results are promising and may be applicable in dealing with clusterings of imbalanced data.