An incremental updating algorithm of attribute reduction set in decision tables

Authors:
Lihe Guan
Affiliations:
Institute of Information and Computing Science, Chongqing Jiaotong University, Chongqing, P.R China
Venue:
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 2
Year:
2009

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Abstract

Rough set theory is a new mathematical approach to imperfect knowledge. Attribute reduction is an important part researched in rough set theory. Many existing algorithms mainly aim at the case of static databases. Very little work has been done in updating of attribute reduction set. In this paper, an incremental updating algorithm of attribute reduction set based on the discernibility matrix element set is proposed in decision tables. When the objects of decision table increase dynamically, the old attribute reduction set can be updated effectively by the changes of discernibility matrix element set. Theoretical analysis and simulation experiments show that the algorithm of this paper is valid, efficient and feasible.