Feature space theory in data mining: transformations between extensions and intensions in knowledge representation

被引:47
作者
Li, HX [1 ]
Xu, LD
Wang, JY
Mo, ZW
机构
[1] Beijing Normal Univ, Dept Math, Beijing 100875, Peoples R China
[2] Sichuan Normal Univ, Dept Math, Chengdu 610066, Peoples R China
[3] Tsing Hua Univ, Dept Automat, Beijing 100084, Peoples R China
[4] Old Dominion Univ, Dept Informat Technol & Decis Sci, Norfolk, VA 23529 USA
[5] Beijing Normal Univ, Sch Informat Sci, Beijing 100875, Peoples R China
[6] Wright State Univ, Dept Management Sci & Informat Syst, Dayton, OH 45435 USA
关键词
feature space; knowledge representation; extensions; intensions; inner projections; fuzzy relation; data mining;
D O I
10.1111/1468-0394.00226
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Knowledge representation is one of the important topics in data mining research. In this paper, based on the feature space theory in data mining, the transformation between extensions and intensions of concepts is discussed in detail. First, inner projections of fuzzy relations, as a basic mathematical tool, are defined, and properties of inner projections are discussed. Then inner transformation of fuzzy relations, inverse inner transformations, and related properties are introduced The concept structure is shown by feature spaces. Lastly, transformations between extensions and intensions are discussed.
引用
收藏
页码:60 / 71
页数:12
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