An Incremental Algorithm Based on Discernibility Matrix for Reducts of Incomplete Decision Tables

被引：0
|
作者：
Zhang, Dedong [1 ]
Li, Renpu [1 ]
Zhang, Fuzeng [1 ]
Zhao, Yongsheng [1 ]
机构：
[1] Ludong Univ, Sch Comp Sci & Technol, Yantai 264025, Peoples R China
来源：
PROGRESS IN INTELLIGENCE COMPUTATION AND APPLICATIONS | 2008年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Attribute reduction is an important issue of data mining. In this paper an incremental algorithm for computing reducts of an incomplete decision table is proposed based on an improved discernibility matrix, which can obtain all reducts through some simple operations on original discernibility matrix when a new object is added to the incomplete decision table. Firstly an improved discernibility function used to generate reducts is presented based on the improved discernibility matrix. And then an incremental method of computing reducts is introduced by analyzing the different cases of the new object. Example shows that the proposed algorithm is very efficient because it can avoid recomputing a new discernibility matrix.
引用
收藏
页码：223 / 227
页数：5
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