Title :
An Improved Apriori Algorithm Based on Association Analysis
Author :
Jia, Yubo ; Xia, Guanghu ; Fan, Hongdan ; Zhang, Qian ; Li, Xu
Abstract :
Association Rules Mining is an important branch of Data Mining Technology, of which Apriori Algorithm is the most influential and classic one. After discussing and analyzing the basic concept of Association Rules Mining, this paper proposes an improved algorithm based on a combination of Data Division and Dynamic Item sets Counting. Analysis of the improved algorithm proves that it can effectively improve the performance of Data Mining.
Keywords :
data mining; performance evaluation; association analysis; association rule mining; data division; data mining technology; dynamic itemsets counting; improved apriori algorithm; performance improvement; Algorithm design and analysis; Association rules; Heuristic algorithms; Itemsets; Apriori Algorithm; Association Rules Mining; Data Division; Data Mining; Dynamic Itemsets Counting;
Conference_Titel :
Networking and Distributed Computing (ICNDC), 2012 Third International Conference on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4673-2858-6
DOI :
10.1109/ICNDC.2012.56
