{"title":"使用新的基于hausdorff的度量聚类不确定区间数据","authors":"M. Zarandi, M. Avazbeigi, M. Anssari, I. Turksen","doi":"10.1109/NAFIPS.2010.5548291","DOIUrl":null,"url":null,"abstract":"This paper presents a new index for measuring interval distances and its related metric. The proposed index and metric are both based on the Hausdorff distance which can be used for clustering uncertain interval data. Then using the new metric, a clustering method is introduced for clustering of intervals. Finally, some experiments are provided to validate the method. Results show that the method can identify appropriate clusters efficiently.","PeriodicalId":394892,"journal":{"name":"2010 Annual Meeting of the North American Fuzzy Information Processing Society","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Clustering uncertain interval data using a new Hausdorff-based metric\",\"authors\":\"M. Zarandi, M. Avazbeigi, M. Anssari, I. Turksen\",\"doi\":\"10.1109/NAFIPS.2010.5548291\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a new index for measuring interval distances and its related metric. The proposed index and metric are both based on the Hausdorff distance which can be used for clustering uncertain interval data. Then using the new metric, a clustering method is introduced for clustering of intervals. Finally, some experiments are provided to validate the method. Results show that the method can identify appropriate clusters efficiently.\",\"PeriodicalId\":394892,\"journal\":{\"name\":\"2010 Annual Meeting of the North American Fuzzy Information Processing Society\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-07-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 Annual Meeting of the North American Fuzzy Information Processing Society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NAFIPS.2010.5548291\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 Annual Meeting of the North American Fuzzy Information Processing Society","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NAFIPS.2010.5548291","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Clustering uncertain interval data using a new Hausdorff-based metric
This paper presents a new index for measuring interval distances and its related metric. The proposed index and metric are both based on the Hausdorff distance which can be used for clustering uncertain interval data. Then using the new metric, a clustering method is introduced for clustering of intervals. Finally, some experiments are provided to validate the method. Results show that the method can identify appropriate clusters efficiently.