{"title":"区间型-2模糊c均值协同聚类方法","authors":"Trong Hop Dang, L. Ngo, W. Pedrycz","doi":"10.1109/FUZZ-IEEE.2015.7337932","DOIUrl":null,"url":null,"abstract":"There have been numerous studies on using the FCM algorithm in clustering and collaboration clustering, especially in data analysis, data mining and pattern recognition. In this study, we present new methods involving interval Type-2 fuzzy sets to realize collaborative clustering. Data in which the clustering results realized at one data site impact clustering carried out at other data sites. Those methods endowed with interval type-2 fuzzy sets help cope with uncertainties present in data. The experiment with weather data sets has shown better results in comparison with the previous approaches.","PeriodicalId":185191,"journal":{"name":"2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"Interval Type-2 fuzzy C-Means approach to collaborative clustering\",\"authors\":\"Trong Hop Dang, L. Ngo, W. Pedrycz\",\"doi\":\"10.1109/FUZZ-IEEE.2015.7337932\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"There have been numerous studies on using the FCM algorithm in clustering and collaboration clustering, especially in data analysis, data mining and pattern recognition. In this study, we present new methods involving interval Type-2 fuzzy sets to realize collaborative clustering. Data in which the clustering results realized at one data site impact clustering carried out at other data sites. Those methods endowed with interval type-2 fuzzy sets help cope with uncertainties present in data. The experiment with weather data sets has shown better results in comparison with the previous approaches.\",\"PeriodicalId\":185191,\"journal\":{\"name\":\"2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)\",\"volume\":\"45 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-11-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FUZZ-IEEE.2015.7337932\",\"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 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FUZZ-IEEE.2015.7337932","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Interval Type-2 fuzzy C-Means approach to collaborative clustering
There have been numerous studies on using the FCM algorithm in clustering and collaboration clustering, especially in data analysis, data mining and pattern recognition. In this study, we present new methods involving interval Type-2 fuzzy sets to realize collaborative clustering. Data in which the clustering results realized at one data site impact clustering carried out at other data sites. Those methods endowed with interval type-2 fuzzy sets help cope with uncertainties present in data. The experiment with weather data sets has shown better results in comparison with the previous approaches.