DocumentCode :
2451126
Title :
A kind of improved algorithm for weighted Apriori and application to data mining
Author :
Shaoqian, Yu
Author_Institution :
Sch. of Comput. & Electron. Eng., Hunan Bus. Coll., Changsha, China
fYear :
2010
fDate :
24-27 Aug. 2010
Firstpage :
507
Lastpage :
510
Abstract :
In data processing of the supermarket, people often use the Apriori algorithm to analyze the customer “shopping basket” Due to the large computation, Apriori algorithm has controlled the number of frequent item sets by using the minimum supporting threshold and pruning techniques, but meaningless frequent item sets still possibly exist. Divide goods into several broad categories and set up the weighted value of categories; Then, calculate the weighted support and confidence, and do pruning and selection according to the minimum weighted support and confidence threshold to get access to the new frequent item sets and association rules and improve the efficiency of the algorithm.
Keywords :
data mining; marketing data processing; set theory; Apriori algorithm; association rule; confidence threshold; data mining; minimum weighted supporting threshold; pruning technique; shopping basket; Algorithm design and analysis; Association rules; Business; Correlation; Itemsets; Improved Apriori algorithm; Shopping Basket analysis; Weighted confidence; Weighted supporting degree;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Science and Education (ICCSE), 2010 5th International Conference on
Conference_Location :
Hefei
Print_ISBN :
978-1-4244-6002-1
Type :
conf
DOI :
10.1109/ICCSE.2010.5593564
Filename :
5593564
Link To Document :
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