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
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;
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
DOI :
10.1109/ICMLC.2008.4620438
