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
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;
Conference_Titel :
Transportation, Mechanical, and Electrical Engineering (TMEE), 2011 International Conference on
Conference_Location :
Changchun
Print_ISBN :
978-1-4577-1700-0
DOI :
10.1109/TMEE.2011.6199453
