Affiliations: [a] Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City, Jiangsu University of Technology, Changzhou, Jiangsu, China | [b] Jiangsu Key Laboratory of Big Data Analysis Technology/B-DAT, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China | [c] School of Computer Engineering and Science, Shanghai University, Shanghai, China
Correspondence:
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Corresponding author: Ping Lv, Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City, Jiangsu University of Technology, Changzhou, Jiangsu 213001, China. E-mail:micheallvping@sina.com
Abstract: Attribute reduction is one of the key issues in rough set
theory, and many algorithms have been proposed for static data set. Very
little work has been done for incremental attribute reduction algorithm in
era of big data. In this paper, the weakness of the existing incremental
attribute reduction are analyzed. Then, two strategies of incremental
attribute reduction algorithm for big data are designed, and an incremental
attribute reduction algorithm using MapReduce is proposed. In order to
reduce the computational complexity, our algorithm reuse the former Map
results to speedup the computations of the equivalence classes. A new reduct
can be updated by the old reduct effectively. This study gives some insights
into how to conduct incremental attribute reduction for big data.
Keywords: Incremental attribute reduction, rough set, big data, MapReduce
