EAV格式大数据和备用数据的粗糙集方法

2012 IEEE RIVF International Conference on Computing & Communication Technologies, Research, Innovation, and Vision for the Future Pub Date : 2012-03-15 DOI:10.1109/rivf.2012.6169830

Wojciech Swieboda, H. Nguyen

{"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}

引用次数: 16

摘要

本文讨论了一种计算大数据集近似决策约简的有效方法。我们考虑了有效存储稀疏数据集的EAV(实体-属性-值)，并对以这种格式表示的数据集提出了最大可分辨性启发式的新实现。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

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.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2012 IEEE RIVF International Conference on Computing & Communication Technologies, Research, Innovation, and Vision for the Future

自引率

0.00%

发文量