PARALLEL KNOWLEDGE ACQUISITION ALGORITHM FOR BIG DATA USING MAPREDUCE

被引：0

作者：

Qian, Jin ^{[1
,2
]}

Xia, Min ^{[2
]}

Lv, Ping ^{[1
]}

机构：

[1] Jiangsu Univ Technol, Key Lab Cloud Comp & Intelligent Informat Proc Ch, Changzhou 213015, Peoples R China

[2] Nanjing Univ Informat Sci & Technol, Jiangsu Key Lab Big Data Anal Technol B DAT, Nanjing, Jiangsu, Peoples R China

来源：

PROCEEDINGS OF 2015 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS (ICMLC), VOL. 1 | 2015年

关键词：

Hierarchical Rough Set; Knowledge Acquisition; MapReduce; Big Data; DECISION TABLES; ROUGH SETS; RULES; LEVEL;

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：

With the rapid growth of data volume, knowledge acquisition for big data has become a new challenge. To address this issue, the hierarchical decision table is defined and implemented in this work. The properties of different hierarchical decision tables are discussed under the different granularity of conditional attributes. A novel knowledge acquisition algorithm for big data using MapReduce is proposed. Experimental results demonstrate that the proposed algorithm is able to deal with big data and mine hierarchical decision rules under the different granularity.

引用

页码：316 / 321

页数：6

共 16 条

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