计算机工程与应用2019，Vol.55Issue(6)：31-38,177,9.DOI:10.3778/j.issn.1002-8331.1809-0126
大数据背景下粗糙集属性约简研究进展
Research Progress of Attribute Reduction Based on Rough Set in Context of Big Data
摘要
Abstract
Classical analysis tools are not able to satisfy this era of big data, which is full of multifarious, complicated and dynamic changed data. How to obtain valuable information from large-scale data quickly and effectively has became a challenging problem. Some scholars combined the rough set attribute reduction theory with other theories to process high-dimensional, dynamic and massive data effectively. The attribute reduction algorithms based on parallel computing, incre-mental learning and granular computing are classified and summarized. Then their characteristics, present problems and the key future research directions are analyzed.关键词
大数据/粗糙集/属性约简/并行计算/增量学习/粒计算Key words
big data/rough set/attribute reduction/parallel computing/incremental learning/granular computing分类
信息技术与安全科学引用本文复制引用
邬阳阳,汤建国..大数据背景下粗糙集属性约简研究进展[J].计算机工程与应用,2019,55(6):31-38,177,9.基金项目
山西省自然科学基金面上项目(No.201601D011043) （No.201601D011043）
国家自然科学基金重点项目(No.11531009). （No.11531009）
