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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/3997

Title: A rough sets based characteristic relation approach for dynamic attribute generalization in data mining
Authors: Li, Tianrui
WETS, Geert
Song, Jing
Xu, Yang
Issue Date: 2007
Citation: KNOWLEDGE-BASED SYSTEMS, 20(5). p. 485-494
Abstract: Any attribute set in an information system may be evolving in time when new information arrives. Approximations of a concept by rough set theory need updating for data mining or other related tasks. For incremental updating approximations of a concept, methods using the tolerance relation and similarity relation have been previously studied in literature. The characteristic relation-based rough sets approach provides more informative results than the tolerance-and-similarity relation based approach. In this paper, an attribute generalization and its relation to feature selection and feature extraction are firstly discussed. Then, a new approach for incrementally updating approximations of a concept is presented under the characteristic relation-based rough sets. Finally, the approach of direct computation of rough set approximations and the proposed approach of dynamic maintenance of rough set approximations are employed for performance comparison. An extensive experimental evaluation on a large soybean database from MLC shows that the proposed approach effectively handles a dynamic attribute generalization in data mining. (C) 2007 Elsevier B.V. All rights reserved.
Notes: SW Jiaotong Univ, Dept Math, Chengdu 610031, Peoples R China. CEN SCK, Belgian Nucl Res Ctr, B-2400 Mol, Belgium. Univ Ghent, Dept Appl Math & Comp Sci, B-9000 Ghent, Belgium. Univ Hasselt, Dept Appl Econ Sci, B-3590 Diepenbeek, Belgium.Li, TR, SW Jiaotong Univ, Dept Math, Chengdu 610031, Peoples R China.trli@swjtu.edu.cn druan@sckcen.be geert.wets@uhasselt.be jesen811206@126.com xuyang@home.swjtu.edu.cn
URI: http://hdl.handle.net/1942/3997
DOI: 10.1016/j.knosys.2007.01.002
ISI #: 000247762200006
ISSN: 0950-7051
Category: A1
Type: Journal Contribution
Validation: ecoom, 2008
Appears in Collections: Research publications

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