{"title":"聚类技术的比较与分析","authors":"B. Singla, K. Yadav, J. Singh","doi":"10.1109/ITSIM.2008.4632059","DOIUrl":null,"url":null,"abstract":"Clustering is in the eye of beholder. Researchers have proposed many principles and models whose corresponding optimization problem can only be approximately solved by even large number of algorithms. Each algorithm emphasizes how it is different from previous algorithms. In our experiment we are comparing hierarchical and partitional clustering in terms of efficiency, intra-cluster similarity, and stability, a desirable property of clustering is stability that is small change to data should not lead dramatically different clustering.","PeriodicalId":314159,"journal":{"name":"2008 International Symposium on Information Technology","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Comparison and analysis of clustering techniques\",\"authors\":\"B. Singla, K. Yadav, J. Singh\",\"doi\":\"10.1109/ITSIM.2008.4632059\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Clustering is in the eye of beholder. Researchers have proposed many principles and models whose corresponding optimization problem can only be approximately solved by even large number of algorithms. Each algorithm emphasizes how it is different from previous algorithms. In our experiment we are comparing hierarchical and partitional clustering in terms of efficiency, intra-cluster similarity, and stability, a desirable property of clustering is stability that is small change to data should not lead dramatically different clustering.\",\"PeriodicalId\":314159,\"journal\":{\"name\":\"2008 International Symposium on Information Technology\",\"volume\":\"12 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-09-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 International Symposium on Information Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ITSIM.2008.4632059\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 International Symposium on Information Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITSIM.2008.4632059","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Clustering is in the eye of beholder. Researchers have proposed many principles and models whose corresponding optimization problem can only be approximately solved by even large number of algorithms. Each algorithm emphasizes how it is different from previous algorithms. In our experiment we are comparing hierarchical and partitional clustering in terms of efficiency, intra-cluster similarity, and stability, a desirable property of clustering is stability that is small change to data should not lead dramatically different clustering.