Title :
The research of improved association rules mining Apriori algorithm
Author :
Huiying Wang ; Xiangwei Liu
Author_Institution :
Sch. of Public Adm., Univ. of Int. Bus. & Econ., Beijing, China
Abstract :
This paper points out the bottleneck of classical Apriori´s algorithm, presents an improved association rule mining algorithm. The new algorithm is based on reducing the times of scanning candidate sets and using hash tree to store candidate itemsets. According to the running result of the algorithm, the processing time of mining is decreased and the efficiency of algorithm has improved.
Keywords :
data mining; candidate itemsets; classical a priori algorithm; hash tree; improved association rule mining algorithm; Algorithm design and analysis; Association rules; Economics; Educational institutions; Itemsets; Apriori algorithm; Data mining; association rule; frequent itemset;
Conference_Titel :
Fuzzy Systems and Knowledge Discovery (FSKD), 2011 Eighth International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-61284-180-9
DOI :
10.1109/FSKD.2011.6019685
