DocumentCode
475941
Title
Attribute reduction of large crisp-real concept lattices
Author
Shao, Ming-wen ; Guo, Ya-li
Author_Institution
Sch. of Inf. Technol., Jiangxi Univ. of Finance & Econ., Nanchang
Volume
1
fYear
2008
fDate
12-15 July 2008
Firstpage
395
Lastpage
400
Abstract
In this paper, we discuss the problems of attribute reduction of large crisp-real concept lattices. We show how to remove redundant attribute from real set formal contexts without loss any of knowledge. By the proposed approach, we remove the attributes which are not essential to the structure of large crisp-real concept lattices.
Keywords
data analysis; knowledge representation; attribute reduction; formal concept analysis; large crisp-real concept lattices; real set formal contexts; Cybernetics; Data analysis; Finance; Fuzzy sets; Information technology; Lattices; Machine learning; Upper bound; Attribute reduction; Concept lattice; Formal concept analysis;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2008 International Conference on
Conference_Location
Kunming
Print_ISBN
978-1-4244-2095-7
Electronic_ISBN
978-1-4244-2096-4
Type
conf
DOI
10.1109/ICMLC.2008.4620438
Filename
4620438
Link To Document