Title :
A type of attribute reduction of formal contexts
Author_Institution :
Sch. of Math., Phys. & Inf. Sci., Zhejiang Ocean Univ., Zhoushan, China
Abstract :
Formal concept analysis is an important mathematical tool for knowledge representation and knowledge discovery. Attribute reduction is a crucial research issue attracting many attention of researchers on formal concept analysis. This paper proposes one type of attribute reduction of formal context, in which all join-irreducible elements of a concept lattice are preserved. The join-irreducible elements of a formal context and their properties are first discussed. Then, the concept of join-irreducible-attribute reduction of formal context is then introduced. Finally, an equivalence condition of join-irreducible-consistent attribute set is given.
Keywords :
data mining; formal concept analysis; knowledge representation; lattice theory; concept lattice; equivalence condition; formal concept analysis; formal contexts; join-irreducible-consistent attribute reduction; knowledge discovery; knowledge representation; mathematical tool; Abstracts; Attribute reduction; concept lattices; formal contexts; join-irreducible elements;
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2012 International Conference on
Conference_Location :
Xian
Print_ISBN :
978-1-4673-1484-8
DOI :
10.1109/ICMLC.2012.6358899