Title :
An Algorithm to Improve the Effectiveness of Apriori
Author :
Sun, Dongme ; Teng, Shaohua ; Zhang, Wei ; Zhu, Haibin
Author_Institution :
Guangdong Univ. of Technol., Guangzhou
Abstract :
Apriori is one of the most important algorithms used in rule association mining. In this paper, we first discuss the limitations of the Apriori algorithm and then propose an enhancement for improving its efficiency. The improved algorithm is based on the combination of forward scan and reverse scan of a given database. If certain conditions are satisfied, the improved algorithm can greatly reduce the scanning times required for the discovery of candidate itemsets. Theoretical proof and analysis are given for the rationality of our algorithm. A simulation instance is given in order to show the advantages of this algorithm compared with Apriori.
Keywords :
data mining; very large databases; Apriori algorithm; association rule mining; candidate itemsets; very large database; Algorithm design and analysis; Association rules; Cognitive informatics; Data mining; Heuristic algorithms; Itemsets; Production; Sun; Transaction databases; Apriori algorithm; Association rule; Data mining; Dynamic itemset counting; Frequent itemset;
Conference_Titel :
Cognitive Informatics, 6th IEEE International Conference on
Conference_Location :
Lake Tahoo, CA
Print_ISBN :
9781-4244-1327-0
Electronic_ISBN :
978-1-4244-1328-7
DOI :
10.1109/COGINF.2007.4341914
