计算机科学与探索Issue(1)：35-45,11.DOI:10.3778/j.issn.1673-9418.1206048
MapReduce框架下并行知识约简算法模型研究
Parallel Algorithm Model for Knowledge Reduction Using MapReduce
摘要
Abstract
Knowledge reduction for massive datasets has attracted many research interests in rough set theory. Clas-sical knowledge reduction algorithms assume that all datasets can be loaded into the main memory of a single machine, which are infeasible for large-scale data. Firstly, this paper analyzes the parallel computations among classical knowledge reduction algorithms. Then, in order to compute the equivalence classes and attribute significance on different candidate attribute sets, it designs and implements the Map and Reduce functions using data and task paral-lelism. Finally, it constructs the parallel algorithm framework model for knowledge reduction using MapReduce, which can be used to compute a reduct for the algorithms based on positive region, discernibility matrix or information entropy. The experimental results demonstrate that the proposed parallel knowledge reduction algorithms can efficiently process massive datasets on Hadoop platform.关键词
MapReduce/粗糙集/知识约简/数据并行/任务并行Key words
MapReduce/rough set/knowledge reduction/data parallel/task parallel分类
信息技术与安全科学引用本文复制引用
钱进,苗夺谦,张泽华,张志飞..MapReduce框架下并行知识约简算法模型研究[J].计算机科学与探索,2013,(1):35-45,11.基金项目
The National Natural Science Foundation of China under Grant Nos.60970061,61075056,61103067(国家自然科学基金) （国家自然科学基金）
the Fun-damental Research Funds for the Central Universities of China （）
(中央高校基本科研业务费专项资金) （中央高校基本科研业务费专项资金）
