[1]王建骅,陈兆芳,郑莉.一种基于概念层次架构的挖掘关联规则及其实证应用[J].福建工程学院学报,2017,15(06):597-605.[doi:10.3969/j.issn.1672-4348.2017.06.018]
 Wang Chien-Hua,Chen Zhaofang,Zheng Li.A mining association rule based on conceptual hierarchy and its applications[J].Journal of FuJian University of Technology,2017,15(06):597-605.[doi:10.3969/j.issn.1672-4348.2017.06.018]
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一种基于概念层次架构的挖掘关联规则及其实证应用()
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《福建工程学院学报》[ISSN:2097-3853/CN:35-1351/Z]

卷:
第15卷
期数:
2017年06期
页码:
597-605
栏目:
出版日期:
2017-12-25

文章信息/Info

Title:
A mining association rule based on conceptual hierarchy and its applications
作者:
王建骅陈兆芳郑莉
福建工程学院管理学院
Author(s):
Wang Chien-Hua Chen Zhaofang Zheng Li
School of Management, Fujian University of Technology
关键词:
数据挖掘 模糊分割法 FP-Growth 关联规则
Keywords:
data mining fuzzy partition FP-Growth association rule
分类号:
TP301.6
DOI:
10.3969/j.issn.1672-4348.2017.06.018
文献标志码:
A
摘要:
在概念层次里进行关联规则的挖掘,并考虑到用户感知与主观判断所产生的认知不确定性;结合模糊分割法与FP-Growth方法,应用于概念层次架构中找出关联规则方法,主要分为两个阶段:层级架构的顺序将数据项做抽象化,找出高频模糊格;由高频模糊格来产生多层次模糊关规则。最后通过比较验证所提方法可提高算法的执行效率、缩短计算时间。
Abstract:
An association rule was explored on the conceptual hierarchy and the cognitive uncertainty caused by users’ perception and subjective judgment was considered. The fuzzy partition method and FP-Growth were combined to mine the association rules on the conceptual hierarchy. It mainly consisted of two phases: the first was to find the high frequency fuzzy patterns by abstracting data items in the order of the hierarchical structure and the second was to generate multiple-level fuzzy association rules from those frequent patterns. Experiments and comparisons with other methods show that the proposed method could improve the efficiency of the algorithm and shorten the computational time.

参考文献/References:

[1] Agrawal R, Srikant R. Fast algorithm for mining association rules in large database[C]∥. Proceeding of 20th International Conference on Very Large Databases. Santiago:[s.n.],1994:478-499.
[2] Park J S, Chen M S, Yu P S. An effective hash based algorithm for mining association rules[C]∥. Proceeding of the 1995 ACM SIGMOD International Conference on Management of Data. San Jose: ACM Press,1995:175-186.
[3] Brin S,Motwani R, Ullman J D, et al. Dynamic itemset counting and implication rules for market basket data[C]∥. ACM SIGMOD International Conference on Management of Data. New York: ACM Press,1997:255-264.
[4] Han J, Pei J, Yin Y. Mining frequent patterns without candidate generation[C]∥. Proceeding 2000 ACM SIGMOD International Conference Management of Data. Dallas: ACM Press,2000:1-12.
[5] Zadeh L A. Fuzzy sets[J].Information and Control,1965,8(3):338-353.
[6] Zadeh L A. The concept of a linguistic variable and its application to approximate reasoning-Ⅰ[J].Information and Science,1975,8(3):199-249.
[7] Zadeh L A. The concept of a linguistic variable and its application to approximate reasoning-Ⅱ[J].Information and Science,1975,8(4):301-357.
[8] Zadeh L A. The concept of a linguistic variable and its application to approximate reasoning Ⅲ[J].Information and Science,1976,9(1):43-80.
[9] Han J W, Fu Y J. Discovery of multiple-level association rules from large database [C]. In Proceedings of International Conference on Very Large Data Bases. Zurich, 1995: 420-431.
[10] Hong T P, Lin K Y, Chien B C. Mining fuzzy multiple-level association rules from quantitative data[J].Applied Intelligence,2003,18(1):79-90.
[11] Hu Y C. Mining association rules at a concept hierarchy using fuzzy partition [J]. Journal of Information Management,2006,13(3):63-80.
[12] Pedrycz W. Triangular membership functions[J].Fuzzy Sets and Systems,1994,64(1):21-30.
[13] Hong T P, Chen C H, Lee Y C, et al. Genetic-fuzzy data mining with divide-and-conquer strategy[J].IEEE Transactions on Evolutionary Computation,2008,12(2):252-265.

更新日期/Last Update: 2017-12-25