Research of Improved FP-Growth Algorithm in Association Rules Mining

被引：23
|
作者：
Zeng, Yi [1 ]
Yin, Shiqun [1 ]
Liu, Jiangyue [1 ]
Zhang, Miao [1 ]
机构：
[1] Southwest Univ, Fac Comp & Informat Sci, Chongqing 400715, Peoples R China
来源：
SCIENTIFIC PROGRAMMING | 2015年 / 2015卷
关键词：
D O I：
10.1155/2015/910281
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
Association rules mining is an important technology in data mining. FP-Growth (frequent-pattern growth) algorithm is a classical algorithm in association rules mining. But the FP-Growth algorithm in mining needs two times to scan database, which reduces the efficiency of algorithm. Through the study of association rules mining and FP-Growth algorithm, we worked out improved algorithms of FP-Growth algorithm-Painting-Growth algorithm and N (not) Painting-Growth algorithm (removes the painting steps, and uses another way to achieve). We compared two kinds of improved algorithms with FP-Growth algorithm. Experimental results show that Painting-Growth algorithm is more than 1050 and N Painting-Growth algorithm is less than 10000 in data volume; the performance of the two kinds of improved algorithms is better than that of FP-Growth algorithm.
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收藏
页数：6
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