Using Fuzzy FP-Growth for Mining Association Rules

被引：0
|
作者：
Wang, Chien-Hua [1 ]
Zheng, Li [1 ]
Yu, Xuelian [1 ]
Zheng, XiDuan [1 ]
机构：
[1] Fujian Univ Technol, Sch Management, Fuzhou 350118, Fujian, Peoples R China
来源：
PROCEEDINGS OF THE 2017 INTERNATIONAL CONFERENCE ON ORGANIZATIONAL INNOVATION (ICOI 2017) | 2017年 / 131卷
关键词：
data mining; fuzzy association rule; FP-growth; CLASSIFICATION PROBLEMS;
D O I：
暂无
中图分类号：
C93 [管理学];
学科分类号：
12 ; 1201 ; 1202 ; 120202 ;
摘要：
This paper aims to use fuzzy set theory and FP-growth derived from fuzzy association rules. At first, we apply fuzzy partition method and decide a membership function of quantitative value for each transaction item. Next, we implement FP-growth to deal with the process of data mining. In addition, in order to understand the impact of fuzzy FP-growth algorithm and other fuzzy data mining algorithms on the execution time and the numbers of generated association rule, the experiment will be performed by using different thresholds. Lastly, the experiment results show fuzzy FP-growth algorithm is more efficient than other existing methods.
引用
收藏
页码：328 / 332
页数：5
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