{"title":"识别集群间的关联规则","authors":"M. Pagani, Gloria Bordogna","doi":"10.1145/1456223.1456307","DOIUrl":null,"url":null,"abstract":"In this paper we introduce a novel procedure, based on (fuzzy) clustering comparison techniques, to identify association rules between clusters.\n The procedure we propose is largely based on the use of clustering comparison techniques that we generalized to the fuzzy context. The described methodology can be useful for exploratory data analysis; its complexity is linear to the number of the entities in the data set.","PeriodicalId":309453,"journal":{"name":"International Conference on Soft Computing as Transdisciplinary Science and Technology","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Identification of association rules between clusters\",\"authors\":\"M. Pagani, Gloria Bordogna\",\"doi\":\"10.1145/1456223.1456307\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we introduce a novel procedure, based on (fuzzy) clustering comparison techniques, to identify association rules between clusters.\\n The procedure we propose is largely based on the use of clustering comparison techniques that we generalized to the fuzzy context. The described methodology can be useful for exploratory data analysis; its complexity is linear to the number of the entities in the data set.\",\"PeriodicalId\":309453,\"journal\":{\"name\":\"International Conference on Soft Computing as Transdisciplinary Science and Technology\",\"volume\":\"24 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-10-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Conference on Soft Computing as Transdisciplinary Science and Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1456223.1456307\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Soft Computing as Transdisciplinary Science and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1456223.1456307","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Identification of association rules between clusters
In this paper we introduce a novel procedure, based on (fuzzy) clustering comparison techniques, to identify association rules between clusters.
The procedure we propose is largely based on the use of clustering comparison techniques that we generalized to the fuzzy context. The described methodology can be useful for exploratory data analysis; its complexity is linear to the number of the entities in the data set.