The New Fast Algorithm Based on Transposed Matrix for Frequent Sets Mining of Association Rule

被引：0
|
作者：
Song Shaoyun [1 ]
Zhang Baohua [1 ]
机构：
[1] Yuxi Normal Univ, Sch Informat Technol & Engn, Yuxi, Yunnan, Peoples R China
来源：
FRONTIERS OF MECHANICAL ENGINEERING AND MATERIALS ENGINEERING II, PTS 1 AND 2 | 2014年 / 457-458卷
关键词：
Transposed Matrix; Association Rules; Data Mining; Unit Matrix;
D O I：
10.4028/www.scientific.net/AMM.457-458.992
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Apriori and its improved algorithms can be generally classified into two kinds: SQL-based and on memory-based. In order to improve association rule mining efficiency, after analyzing the efficiency bottlenecks in some algorithms of the second class, an improved efficient algorithm is proposed. Two matrixes are introduced into the algorithm: one is used to map database and the other to store frequent 2-itemsets related information. Through the operation of two matrixes, its time complexity and space complexity decrease significantly. The experiment indicates that the method has better performance.
引用
收藏
页码：992 / 997
页数：6
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