A parallel method for computing rough set approximations

Junbo Zhang; Tianrui Li; Da Ruan; Zizhe Gao; Chengbing Zhao

doi:10.1016/j.ins.2011.12.036

A parallel method for computing rough set approximations

Junbo Zhang, Tianrui Li, Da Ruan, Zizhe Gao, Chengbing Zhao

Research output › peer-review

Abstract

Massive data mining and knowledge discovery present a tremendous challenge with the data volume growing at an unprecedented rate. Rough set theory has been successfully applied in data mining. The lower and upper approximations are basic concepts in rough set theory. The effective computation of approximations is vital for improving the performance of data mining or other related tasks. The recently introduced MapReduce technique has gained a lot of attention from the scientific community for its applicability in massive data analysis. This paper proposes a parallel method for computing rough set approximations. Consequently, algorithms corresponding to the parallel method based on the MapReduce technique are put forward to deal with the massive data. An extensive experimental evaluation on different large data sets shows that the proposed parallel method is effective for data mining.

Original language	English
Pages (from-to)	209-223
Number of pages	15
Journal	Information Sciences
Volume	194
DOIs	https://doi.org/10.1016/j.ins.2011.12.036
State	Published - 1 Jul 2012

Funding

Funders	Funder number
Doctoral Innovation Foundation of Southwest Jiaotong University	2012ZJB
NSFC - National Natural Science Foundation of China	61175047, 60873108, 61100117

ASJC Scopus subject areas

Software
Control and Systems Engineering
Theoretical Computer Science
Computer Science Applications
Information Systems and Management
Artificial Intelligence

Access to Document

10.1016/j.ins.2011.12.036License: Unspecified

Cite this

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title = "A parallel method for computing rough set approximations",

abstract = "Massive data mining and knowledge discovery present a tremendous challenge with the data volume growing at an unprecedented rate. Rough set theory has been successfully applied in data mining. The lower and upper approximations are basic concepts in rough set theory. The effective computation of approximations is vital for improving the performance of data mining or other related tasks. The recently introduced MapReduce technique has gained a lot of attention from the scientific community for its applicability in massive data analysis. This paper proposes a parallel method for computing rough set approximations. Consequently, algorithms corresponding to the parallel method based on the MapReduce technique are put forward to deal with the massive data. An extensive experimental evaluation on different large data sets shows that the proposed parallel method is effective for data mining.",

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AU - Zhao, Chengbing

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