An improved Apriori algorithm based on adjacency list and index

被引：0
|
作者：
Duan Longhen [1 ]
Mang Feng [1 ]
Tong Feng [1 ]
Huang Longjun [1 ]
Qiu Xingxing [1 ]
机构：
[1] Univ Nanchang, Dept Comp Sci, Nanchang 330031, Jiangxi Prov, Peoples R China
来源：
Advanced Computer Technology, New Education, Proceedings | 2007年
关键词：
data mining; association rule; frequent itemsets; apriori algorithm;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Association rule mining is one of the key technologies in data mining. According to the limitations of Apriori algorithm, this paper proposes a new improved Apriori algorithm based on adjacency list and index in which the database is scanned only once and frequent K-itemsets are generated directly. It can avoid generating candidate frequent itemsets and prune operation. A practical example is given and the results of experiment show that the improved algorithm is more efficient.
引用
收藏
页码：239 / 244
页数：6
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