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
Link To Document :
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