# [1]陆建江,徐宝文,邹晓峰,等.模糊关联规则的并行挖掘算法[J].东南大学学报(自然科学版),2005,35(2):165-170.[doi:10.3969/j.issn.1001-0505.2005.02.001] 　Lu Jianjiang,Xu Baowen,Zou Xiaofeng,et al.Parallel mining algorithm for fuzzy association rules[J].Journal of Southeast University (Natural Science Edition),2005,35(2):165-170.[doi:10.3969/j.issn.1001-0505.2005.02.001] 点击复制 模糊关联规则的并行挖掘算法() 分享到： var jiathis_config = { data_track_clickback: true };

35

2005年第2期

165-170

2005-03-20

## 文章信息/Info

Title:
Parallel mining algorithm for fuzzy association rules

1 东南大学计算机科学与工程系, 南京 210096; 2 江苏省软件质量研究所, 南京 210096; 3 解放军理工大学通信工程学院, 南京 210007
Author(s):
1 Department of Computer Science and Engineering, Southeast University, Nanjing 210096, China
2 Jiangsu Institute of Software Quality, Nanjing 210096, China
3 Institute of Communication Engineering, PLA University of Science and Technology, Nanjing 210007, China

Keywords:

TP311.13
DOI:
10.3969/j.issn.1001-0505.2005.02.001

Abstract:
A parallel algorithm for mining fuzzy association rules is presented and its principle and steps are discussed. In this algorithm, the quantitative attributes are partitioned into several fuzzy sets by parallel fuzzy c-means algorithm. By means of fuzzy sets the partition boundary of the attributes is softened. Then, the parallel algorithm for mining Boolean association rules is improved to discover frequent fuzzy attributes. Finally, the fuzzy association rules with least fuzzy confidence are generated by all processors. This algorithm has been implemented at distributively linked PC/workstation. The experiment results show that the parallel mining algorithm has fine scaleup, sizeup and speedup.

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