{"title":"一种改进的基于链接分析的聚类集成方法","authors":"Li-Juan Wang, Z. Hao","doi":"10.1109/ICMLC.2012.6358884","DOIUrl":null,"url":null,"abstract":"This paper proposes an improved link analysis based clustering ensemble method (ILCEM). ILCEM can transform binary data-cluster association matrix into real-valued matrix according to the similarity between clusters in all base clustering. The refined data-cluster association matrix can generate more information to clustering ensemble so as to improve the performance of clustering. Experimental results on three VCI datasets have shown that ILCEM is better than KMC, base clustering method and CSM+GKMC.","PeriodicalId":128006,"journal":{"name":"2012 International Conference on Machine Learning and Cybernetics","volume":"56 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An improved link analysis based clustering ensemble method\",\"authors\":\"Li-Juan Wang, Z. Hao\",\"doi\":\"10.1109/ICMLC.2012.6358884\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes an improved link analysis based clustering ensemble method (ILCEM). ILCEM can transform binary data-cluster association matrix into real-valued matrix according to the similarity between clusters in all base clustering. The refined data-cluster association matrix can generate more information to clustering ensemble so as to improve the performance of clustering. Experimental results on three VCI datasets have shown that ILCEM is better than KMC, base clustering method and CSM+GKMC.\",\"PeriodicalId\":128006,\"journal\":{\"name\":\"2012 International Conference on Machine Learning and Cybernetics\",\"volume\":\"56 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-07-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 International Conference on Machine Learning and Cybernetics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMLC.2012.6358884\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 International Conference on Machine Learning and Cybernetics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLC.2012.6358884","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An improved link analysis based clustering ensemble method
This paper proposes an improved link analysis based clustering ensemble method (ILCEM). ILCEM can transform binary data-cluster association matrix into real-valued matrix according to the similarity between clusters in all base clustering. The refined data-cluster association matrix can generate more information to clustering ensemble so as to improve the performance of clustering. Experimental results on three VCI datasets have shown that ILCEM is better than KMC, base clustering method and CSM+GKMC.