DocumentCode
2074357
Title
A Research about mining association rules based on Quantitative Concept Lattice
Author
Shangping, Dai ; Na, Li
Author_Institution
Dept. of Comput. Sci., HuaZhong Normal Univ., Wuhan, China
fYear
2011
fDate
16-18 Dec. 2011
Firstpage
1337
Lastpage
1340
Abstract
One of the important branches in data mining is association rules mining, the traditional Apriori algorithm has some drawbacks, a method of mining association rules based on Quantitative Concept Lattice (QCL) is presented in this paper, the method can get the support degree of frequent items through Hasse figure and then extract association rules, therefore the data mining efficiency is improved.
Keywords
data mining; Hasse figure; QCL; association rule extraction; association rule mining; data mining; frequent items; quantitative concept lattice; Algorithm design and analysis; Association rules; Itemsets; Knowledge engineering; Lattices; Apriori algorithm; Association rules; Hasse figure; Quantitative Concept Lattice; data mining; support degree;
fLanguage
English
Publisher
ieee
Conference_Titel
Transportation, Mechanical, and Electrical Engineering (TMEE), 2011 International Conference on
Conference_Location
Changchun
Print_ISBN
978-1-4577-1700-0
Type
conf
DOI
10.1109/TMEE.2011.6199453
Filename
6199453
Link To Document