DocumentCode :
2010805
Title :
Mining association rules based on an improved Apriori Algorithm
Author :
Zhou, Yanfei ; Wan, Wanggen ; Liu, Junwei ; Cai, Long
Author_Institution :
Sch. of Commun. & Inf. Eng., Shanghai Univ., Shanghai, China
fYear :
2010
fDate :
23-25 Nov. 2010
Firstpage :
414
Lastpage :
418
Abstract :
In this paper, we first describe the classical Apriori Algorithm. And then, we present the defects exist in this algorithm. Such as spending a lot of time to produce the candidate item-sets, scanning the database in a simple method, and so on. At last we promote our improved Apriori Algorithm which consists of three parts. The first part is reducing the number of judgments, and the second part is reducing the number of candidate frequent item-sets. The last part is optimizing the database. And the experimental results proved our improvement.
Keywords :
data mining; candidate frequent item set; improved Apriori Algorithm; mining association rules; Algorithm design and analysis; Association rules; Complexity theory; Joining processes; Transaction databases;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Audio Language and Image Processing (ICALIP), 2010 International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-5856-1
Type :
conf
DOI :
10.1109/ICALIP.2010.5684546
Filename :
5684546
Link To Document :
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