[1]杨明,杨萍,吉根林,等.一种基于关联规则的缺省规则挖掘算法[J].东南大学学报(自然科学版),2003,33(6):689-693.[doi:10.3969/j.issn.1001-0505.2003.06.003]
 Yang Ming,Yang Ping,Ji Genlin,et al.Algorithm for mining default decision rules based on association rules[J].Journal of Southeast University (Natural Science Edition),2003,33(6):689-693.[doi:10.3969/j.issn.1001-0505.2003.06.003]
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一种基于关联规则的缺省规则挖掘算法()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
33
期数:
2003年第6期
页码:
689-693
栏目:
计算机科学与工程
出版日期:
2003-11-20

文章信息/Info

Title:
Algorithm for mining default decision rules based on association rules
作者:
杨明12 杨萍1 吉根林2 孙志挥2
1 安徽工程科技学院, 芜湖 241000; 2 东南大学计算机科学与工程系, 南京 210096
Author(s):
Yang Ming12 Yang Ping1 Ji Genlin2 Sun Zhihui2
1 Anhui University of Technology and Science, Wuhu 241000, China
2 Department of Computer Science and Engineering, Southeast University, Nanjing 210096, China
关键词:
Rough 集 缺省规则 关联规则 相容关联规则
Keywords:
Rough set default rules association rules compatible association rules
分类号:
TP311
DOI:
10.3969/j.issn.1001-0505.2003.06.003
摘要:
传统的基于Rough集的缺省规则挖掘算法须计算差别矩阵并生成大量的条件属性类,挖掘效率低. 为此,本文引入相容关联规则和决策关联规则的概念,提出基于关联规则的缺省规则挖掘算法——DRMBAR,该算法借助FP-tree存储结构挖掘出决策关联规则,并用相容关联规则性质对决策关联规则进行有效修剪后生成相应的缺省规则.DRMBAR可有效地过滤噪声、提高缺省规则挖掘效率,且克服了传统算法依赖于主存的限制,为缺省规则的挖掘提供了一种新的框架.实验结果表明该算法是有效且可行的.
Abstract:
Conventional algorithms of mining uncertain decision rules based on Rough sets need to compute the time-consuming discernibility matrix and generate lots of attribute classes, thus they are in low efficiency. In this paper, we introduce the concepts of compatible association rules and decision association rules, and propose an algorithm for mining default rules based on association rules — DRMBAR, which mines decision association rules by using the FP-tree structure and prunes the superfluous decision association rules for generating default rules. DRMBAR can effectively filter out noise and overcome the limitation of main memory, so the problem of time-consuming default rules is solved. Therefore, DRMBAR provides a new framework for mining default rules. Experiment results show that DRMBAR is efficient and effective.

参考文献/References:

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备注/Memo

备注/Memo:
基金项目: 国家自然科学基金资助项目(79970092)、安徽省自然科学基金资助项目(03042205)、安徽省教育厅教学研究资助项目(2003kj029).
作者简介: 杨明(1964—),男,博士生,副教授; 孙志辉(联系人),男,教授,博士生导师,sunzh@seu.edu.cn.
更新日期/Last Update: 2003-11-20