Paper Info
Title
An Effective Algorithm Based on Association Graph and Matrix for Mining Association Rules
Abstract
Association rule mining is a very important research topic in the field of data mining. Discovering frequent itemsets is the key process in association rule mining. Traditional association rule algorithms adopt an iterative method, which requires very large calculations and a complicated transaction process. FAR (Feature Matrix Based Association Rules) algorithm solves this problem. However, FAR algorithm is not efficient when the value of the minimum support is small or the number of column of the feature matrix is very large. So we proposed a new algorithm (GMA) which based on association graph and matrix pruning to reduce the amount of candidate itemsets. Experimental results show that our algorithm is more efficient for different values of minimum support.
Year
DOI
Venue
2010
10.1109/DBTA.2010.5659019
DBTA
Keywords
Field
DocType
matrix pruning,association graph,association rule,relational calculus,matrix algebra,far algorithm,feature matrix,medical image,data mining,graph theory,association rules,algorithm design and analysis,calculus,association rule mining,biomedical imaging,transaction processing,iteration method
Data mining,Matrix (mathematics),Computer science,FSA-Red Algorithm,Artificial intelligence,Feature matrix,Graph theory,Graph,Algorithm design,Iterative method,Algorithm,Association rule learning,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-4244-6977-2
0
0.34
References 
Authors
7
4
Name
Order
Citations
PageRank
Haiwei Pan15221.31
Xiaolei Tan220.70
Qilong Han315619.26
Guisheng Yin419514.69