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Feature space theory in data mining: transformations between extensions and intensions in knowledge representation
Author(s) -
Li Hong Xing,
Xu Li Da,
Wang Jia Yin,
Mo Zhi Wen
Publication year - 2003
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/1468-0394.00226
Subject(s) - computer science , transformation (genetics) , representation (politics) , feature (linguistics) , space (punctuation) , fuzzy logic , data mining , artificial intelligence , theoretical computer science , biochemistry , chemistry , linguistics , philosophy , politics , political science , law , gene , operating system
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.