CAJ | 학술논문

上、下近似空间是粗糙理论的重要概念，解决上、下近似问题是海量数据挖掘的基础。经典的近似空间算法不适合处理海量数据，更不适合处理带缺失信息的海量数据问题。为此，通过深度分析带缺失信息的海量数据特征，结合MapReduce编程模型，提出了基于MapReduce框架下近似空间的并行算法，以处理带缺失信息的海量数据，实验结果表明了该并行算法的有效性。
상、하근사공간시조조이론적중요개념，해결상、하근사문제시해량수거알굴적기출。경전적근사공간산법불괄합처리해량수거，경불괄합처리대결실신식적해량수거문제。위차，통과심도분석대결실신식적해량수거특정，결합MapReduce편정모형，제출료기우MapReduce광가하근사공간적병행산법，이처리대결실신식적해량수거，실험결과표명료해병행산법적유효성。
The lower and upper approximations are important concepts in rough set theory. Therefore, the computation of approximations is the basic for improving the massive data mining performance. Classical approximation space algorithm is infeasible for massive data, much less for massive data with missing information. To this end, through deep analysis of the characteristics of massive data with missing information, combining with the MapReduce programming model, a par-allel algorithm for computing incomplete information systems using MapReduce is put forward to deal with the massive data with missing information. The experimental results demonstrate that the proposed parallel algorithm is effective.