{"title":"收缩相关聚类","authors":"László Aszalós, Tamás Mihálydeák","doi":"10.15439/2015F137","DOIUrl":null,"url":null,"abstract":"We suggest an effective method for solving the problem of correlation clustering. This method is based on an extension of a partial tolerance relation to clusters. We present several implementation of this method using different data structures, and we show a method to speed up the execution by a quasi-parallelism.","PeriodicalId":276884,"journal":{"name":"2015 Federated Conference on Computer Science and Information Systems (FedCSIS)","volume":"143 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Correlation clustering by contraction\",\"authors\":\"László Aszalós, Tamás Mihálydeák\",\"doi\":\"10.15439/2015F137\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We suggest an effective method for solving the problem of correlation clustering. This method is based on an extension of a partial tolerance relation to clusters. We present several implementation of this method using different data structures, and we show a method to speed up the execution by a quasi-parallelism.\",\"PeriodicalId\":276884,\"journal\":{\"name\":\"2015 Federated Conference on Computer Science and Information Systems (FedCSIS)\",\"volume\":\"143 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-11-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 Federated Conference on Computer Science and Information Systems (FedCSIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.15439/2015F137\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 Federated Conference on Computer Science and Information Systems (FedCSIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.15439/2015F137","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We suggest an effective method for solving the problem of correlation clustering. This method is based on an extension of a partial tolerance relation to clusters. We present several implementation of this method using different data structures, and we show a method to speed up the execution by a quasi-parallelism.