DocumentCode :
2826810
Title :
Quantitative Association Rules Mining Algorithm Based on Matrix
Author :
Liu, Huizhen ; Dai, Shangping ; Jiang, Hong
Author_Institution :
Dept. of Comput. Sci., Huazhong Normal Univ., Wuhan, China
fYear :
2009
fDate :
11-13 Dec. 2009
Firstpage :
1
Lastpage :
4
Abstract :
How to improve the efficiency of discovering the frequent item sets is a major problem in mining association rules. This paper analysised the idea and performance of the general quantitative association rules algorithm ,and put forward a quantitative association rules mining algorithm based on matrix, the new algorithm firstly transformed quantitative database into Boolean matrix ,then used boolean "and" operation to generate frequent item sets on matrix vector .It effectively solved the bottleneck of Apriori algorithm which iteratively produced frequent item sets in the general quantitative association rules algorithm . The results of experiments and analysis showed that the new algorithm effectively improved the efficiency of mining quantitative association rules.
Keywords :
Boolean algebra; data mining; matrix algebra; Boolean matrix; iteratively produced frequent item sets; matrix vector; quantitative association rules mining algorithm; Algorithm design and analysis; Artificial intelligence; Association rules; Computer science; Data mining; Iterative algorithms; Machine learning algorithms; Performance analysis; Relational databases; Transaction databases;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-4507-3
Electronic_ISBN :
978-1-4244-4507-3
Type :
conf
DOI :
10.1109/CISE.2009.5363896
Filename :
5363896
Link To Document :
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