计算机技术与发展Issue(1)：137-142,6.DOI:10.3969/j.issn.1673-629X.2015.01.031
海量数据下不完备信息系统的知识约简算法
Knowledge Reduction Algorithms of Incomplete Information System in Massive Datasets
摘要
Abstract
Knowledge reduction for massive datasets has attracted many research interests in rough set theory. Traditional knowledge re-duction algorithms of incomplete information system assume that all the datasets can be loaded into the main memory,which are obvious-ly infeasible for large-scale datasets,especially for massive datasets with missing information. To this end,deeply analyze the characteris-tics of massive datasets with missing information,and allow the missing attribute value to take all possible values. Then,by combining the parallel computations used in classical knowledge reduction algorithms with the discernibility ( indiscernibility) of the attributes,a knowl-edge reduction algorithm is designed for incomplete information systems under MapReduce framework. The experimental results demon-strate that this algorithm is effective and feasible,which can efficiently process massive datasets for knowledge reduction in incomplete in-formation systems.关键词
海量数据/云计算/粗糙集/不完备信息系统/约简/MapReduceKey words
massive data/cloud computing/rough set/incomplete information system/reduction/MapReduce分类
信息技术与安全科学引用本文复制引用
王添,姜麟,米允龙..海量数据下不完备信息系统的知识约简算法[J].计算机技术与发展,2015,(1):137-142,6.基金项目
云南省教育科研基金(2010Y389) （2010Y389）
