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

页码：239 / 244

页数：6

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