{"title":"基于梯形云模型的定量关联规则挖掘方法","authors":"Zhao-hong Wang","doi":"10.1109/DBTA.2010.5658963","DOIUrl":null,"url":null,"abstract":"The quantitative association rules mining method is difficult for their values are too large. The usual means is dividing quantitative Data to discrete conception. The trapezium Cloud model combines ambiguity and randomness organically to fit the real world objectively, divide quantitative Data with trapezium Cloud model to create concepts, the concept cluster within one class, and separated with each other. So the quantitative Data can be transforms to Boolean data well, the Boolean data can be mined by the mature Boolean association rules mining method.","PeriodicalId":320509,"journal":{"name":"2010 2nd International Workshop on Database Technology and Applications","volume":"66 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Quantitative Association Rules Mining Method Based on Trapezium Cloud Model\",\"authors\":\"Zhao-hong Wang\",\"doi\":\"10.1109/DBTA.2010.5658963\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The quantitative association rules mining method is difficult for their values are too large. The usual means is dividing quantitative Data to discrete conception. The trapezium Cloud model combines ambiguity and randomness organically to fit the real world objectively, divide quantitative Data with trapezium Cloud model to create concepts, the concept cluster within one class, and separated with each other. So the quantitative Data can be transforms to Boolean data well, the Boolean data can be mined by the mature Boolean association rules mining method.\",\"PeriodicalId\":320509,\"journal\":{\"name\":\"2010 2nd International Workshop on Database Technology and Applications\",\"volume\":\"66 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-12-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 2nd International Workshop on Database Technology and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DBTA.2010.5658963\",\"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 2nd International Workshop on Database Technology and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DBTA.2010.5658963","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Quantitative Association Rules Mining Method Based on Trapezium Cloud Model
The quantitative association rules mining method is difficult for their values are too large. The usual means is dividing quantitative Data to discrete conception. The trapezium Cloud model combines ambiguity and randomness organically to fit the real world objectively, divide quantitative Data with trapezium Cloud model to create concepts, the concept cluster within one class, and separated with each other. So the quantitative Data can be transforms to Boolean data well, the Boolean data can be mined by the mature Boolean association rules mining method.