Title :
A Study of Improving Apriori Algorithm
Author :
Wu, Libing ; Gong, Kui ; He, Yanxiang ; Ge, Xiaohua ; Cui, Jianqun
Author_Institution :
Sch. of Comput., Wuhan Univ., Wuhan, China
Abstract :
The Apriori algorithm is one of the most influential apriori for mining association rules. The basic idea of the Apriori algorithm is to identify all the frequent sets. Through the frequent sets, derived association rules, these rules must satisfy minimum support threshold and minimum confidence threshold. This paper presents improved algorithms, mainly through the introduction of interest items, frequency threshold, to improve the mining efficiency, dynamic data mining to facilitate the needs of users. Confirmed by many experiments, this algorithm is better than traditional algorithms in time and space complexity.
Keywords :
data mining; Apriori algorithm; association rule mining; dynamic data mining; frequency threshold; space complexity; time complexity; Advertising; Algorithm design and analysis; Association rules; Computer science; Data mining; Frequency; Helium; Heuristic algorithms; Itemsets; Transaction databases;
Conference_Titel :
Intelligent Systems and Applications (ISA), 2010 2nd International Workshop on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-5872-1
Electronic_ISBN :
978-1-4244-5874-5
DOI :
10.1109/IWISA.2010.5473450
