DocumentCode
424104
Title
The analysis on model of association rules mining based on concept lattice and Apriori algorithm
Author
Hu, Xue-Gang ; Wang, De-Xing ; Liu, Xiao-Ping ; Guo, Un ; Wang, Hao
Author_Institution
Dept. of Comput. Sci. & Technol., Hefei Univ. of Technol., China
Volume
3
fYear
2004
fDate
26-29 Aug. 2004
Firstpage
1620
Abstract
Concept lattice represents knowledge with the relationships between the intension and the extension of concepts, and the relationship between the generalization and the specialization of concepts, thus it is properly applied to the description of association rules mining in database. The quantitative extended concept lattice (QECL) evolves from concept lattice by introducing equivalent relationships to its intension and quantity to its extension, based on it, we can mine association rules, compared with the well-known Apriori algorithm, without calculating frequent itemsets, easily obtain interesting association rules, in the meantime a lot of redundant rules are reduced, thus the efficiency and veracity of the mining rules are improved.
Keywords
data mining; knowledge representation; set theory; Apriori algorithm; association rules mining; knowledge representation; quantitative extended concept lattice; set theory; Algorithm design and analysis; Association rules; Computer science; Data mining; Databases; Itemsets; Iterative algorithms; Lattices; Machine learning algorithms; Nuclear and plasma sciences;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on
Print_ISBN
0-7803-8403-2
Type
conf
DOI
10.1109/ICMLC.2004.1382034
Filename
1382034
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