DocumentCode
3182103
Title
Mining Association Rules Based on Apriori Algorithm and Application
Author
Pei-ji Wang ; Lin Shi ; Jin-niu Bai ; Yu-lin Zhao
Author_Institution
Sch. of Math., Phys. & Biol. Eng., Inner Mongolia Univ. of Sci. & Technol., Baotou, China
Volume
1
fYear
2009
fDate
25-27 Dec. 2009
Firstpage
141
Lastpage
143
Abstract
In the data mining research, mining association rules is an important topic. Apriori algorithm submitted by Agrawal and R. Srikant in 1994 is the most effective algorithm. Aimed at two problems of discovering frequent itemsets in a large database and mining association rules from frequent itemsets, the author makes some research on mining frequent itemsets algorithm based on apriori algorithm and mining association rules algorithm based on improved measure system. Mining association rules algorithm based on support, confidence and interestingness is improved, aiming at creating interestingness useless rules and losing useful rules. Useless rules are cancelled, creating more reasonable association rules including negative items. The above method is used to mine association rules to the 2002 student score list of computer specialized field in Inner Mongolia university of science and technology.
Keywords
data mining; very large databases; apriori algorithm; association rule mining; data mining; frequent itemset discovery; interestingness useless rule; large database; losing useful rule; measure system; Application software; Association rules; Biology computing; Computer applications; Data mining; Databases; Information systems; Itemsets; Mathematics; Physics computing; application; apriori algorithm; association rules mining; recognizable matrix;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science-Technology and Applications, 2009. IFCSTA '09. International Forum on
Conference_Location
Chongqing
Print_ISBN
978-0-7695-3930-0
Electronic_ISBN
978-1-4244-5423-5
Type
conf
DOI
10.1109/IFCSTA.2009.41
Filename
5385112
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