{"title":"EAV格式大数据和备用数据的粗糙集方法","authors":"Wojciech Swieboda, H. Nguyen","doi":"10.1109/rivf.2012.6169830","DOIUrl":null,"url":null,"abstract":"In this article we discuss a computationally effective method for computing approximate decision reducts of large data sets. We consider the EAV (entity-attribute-value) which efficiently stores sparse data sets and we propose new implementations of Maximum Discernibility heuristic for data sets represented in this format.","PeriodicalId":115212,"journal":{"name":"2012 IEEE RIVF International Conference on Computing & Communication Technologies, Research, Innovation, and Vision for the Future","volume":"73 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":"{\"title\":\"Rough Set Methods for Large and Spare Data in EAV Format\",\"authors\":\"Wojciech Swieboda, H. Nguyen\",\"doi\":\"10.1109/rivf.2012.6169830\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this article we discuss a computationally effective method for computing approximate decision reducts of large data sets. We consider the EAV (entity-attribute-value) which efficiently stores sparse data sets and we propose new implementations of Maximum Discernibility heuristic for data sets represented in this format.\",\"PeriodicalId\":115212,\"journal\":{\"name\":\"2012 IEEE RIVF International Conference on Computing & Communication Technologies, Research, Innovation, and Vision for the Future\",\"volume\":\"73 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-03-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"16\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE RIVF International Conference on Computing & Communication Technologies, Research, Innovation, and Vision for the Future\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/rivf.2012.6169830\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE RIVF International Conference on Computing & Communication Technologies, Research, Innovation, and Vision for the Future","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/rivf.2012.6169830","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Rough Set Methods for Large and Spare Data in EAV Format
In this article we discuss a computationally effective method for computing approximate decision reducts of large data sets. We consider the EAV (entity-attribute-value) which efficiently stores sparse data sets and we propose new implementations of Maximum Discernibility heuristic for data sets represented in this format.