关键词: MapReduce, 数据挖掘, 海量数据, 粗糙集, 不完备信息系统, 近似空间

Abstract: 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 parallel 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.

Key words: MapReduce, data mining, massive data, rough set, incomplete information system, approximations