Neighborhood rough sets for dynamic data mining

Authors:
Junbo Zhang;Tianrui Li;Da Ruan;Dun Liu
Affiliations:
School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, People's Republic of China;School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, People's Republic of China;Belgian Nuclear Research Centre (SCK CEN), Boeretang 200, 2400 Mol, Belgium and Department of Applied Mathematics & Computer Science, Ghent University, 9000 Gent, Belgium;School of Economics and Management, Southwest Jiaotong University, Chengdu 610031, People's Republic of China
Venue:
International Journal of Intelligent Systems
Year:
2012

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Abstract

Approximations of a concept in rough set theory induce rules and need to update for dynamic data mining and related tasks. Most existing incremental methods based on the classical rough set model can only be used to deal with the categorical data. This paper presents a new dynamic method for incrementally updating approximations of a concept under neighborhood rough sets to deal with numerical data. A comparison of the proposed incremental method with a nonincremental method of dynamic maintenance of rough set approximations is conducted by an extensive experimental evaluation on different data sets from UCI. Experimental results show that the proposed method effectively updates approximations of a concept in practice. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.