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A Group Incremental Reduction Algorithm with Varying Data Values
Author(s) -
Jing Yunge,
Li Tianrui,
Huang Junfu,
Chen Hongmei,
Horng ShiJinn
Publication year - 2017
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.21876
Subject(s) - reduct , rough set , reduction (mathematics) , decision table , attribute domain , data mining , granularity , computer science , data reduction , algorithm , set (abstract data type) , variation (astronomy) , mathematics , physics , geometry , astrophysics , programming language , operating system
Attribute reduction based on rough set theory has attracted much attention recently. In real‐life applications, many decision tables may vary dynamically with time, e.g., the variation of attributes, objects, and attribute values. The reduction of decision tables may change on the alteration of attribute values. The paper focuses on dynamic maintenance of attribute reduction when varying data values of multiple objects. Incremental mechanisms for knowledge granularity are proposed first, which aims to update attribute reduction effectively. Then, a group incremental reduction algorithm with varying data values is developed. When attribute values of multiple objects have been replaced by new ones in decision table, the proposed incremental algorithm can find the new reduct in a much shorter time. The time complexity analysis and experiments on different data sets from UCI have validated that the proposed incremental algorithms are efficient and effective to update the reduction with the variation of attribute values.