Reducts in incomplete decision tables

被引：0
|
作者：
Li, RP [1 ]
Huang, D
机构：
[1] Yantai Normal Univ, Coll Comp Sci & Technol, Yantai 264025, Peoples R China
[2] E China Univ Sci & Technol, Coll Informat, Shanghai 200237, Peoples R China
来源：
ADVANCED DATA MINING AND APPLICATIONS, PROCEEDINGS | 2005年 / 3584卷
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：
Knowledge reduction is an important issue in data mining. This paper focuses on the problem of knowledge reduction in incomplete decision tables. Based on a concept of incomplete conditional entropy, a new reduct definition is presented for incomplete decision tables and its properties are analyzed. Compared with several existing reduct definitions, the new definition has a better explanation for knowledge uncertainty and is more convenient for application of the idea of approximate reduct in incomplete decision tables.
引用
收藏
页码：165 / 174
页数：10
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