Attribute Reduction for Massive Data Based on Rough Set Theory and MapReduce

被引：0

作者：

Yang, Yong ^{[1
]}

Chen, Zhengrong ^{[1
]}

Liang, Zhu ^{[1
]}

Wang, Guoyin ^{[1
]}

机构：

[1] Chongqing Univ Posts & Telecommun, Inst Comp Sci & Technol, Chongqing 400065, Peoples R China

来源：

ROUGH SET AND KNOWLEDGE TECHNOLOGY (RSKT) | 2010年 / 6401卷

关键词：

Attribute reduction; rough set; MapReduce;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Data processing and knowledge discovery for massive data is always a hot topic in data mining, along with the era of cloud computing is coming, data mining for massive data is becoming a highlight research topic. In this paper, attribute reduction for massive data based on rough set theory is studied. The parallel programming mode of MapReduce is introduced and combined with the attribute reduction algorithm of rough set theory, a parallel attribute reduction algorithm based on MapReduce is proposed, experiment results show that the proposed method is more efficiency for massive data mining than traditional method, and it is a effective method effective method effective method for data mining on cloud computing platform.

引用

页码：672 / 678

页数：7

共 12 条

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