[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1; 2;  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 Ming1; 2;  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:

[1] Pawlak Z.Rough sets[J]. International Journal of Information and Computer Science, 1982,11(5):341-356.
[2] Wang Jue,Wang Ju.Reduction algorithm based on discernibility matrix the ordered attributes method[J]. Journal of Computer Science & Technology,2001,16(6):489-504.
[3] Chmielewski M R,Grzymala-Busse J W.Global discretization of continuous attributes as preprocessing for machine learning[J]. International Journal of Approximate Reasoning,1996(15):319-331.
[4] Kryszkiewicz M.Rules in incomplete information systems [J]. Information Sciences,1999,11(3):271-292.
[5] Tsumoto S.Automated discovery of positive and negative knowledge in clinical databases:a rule — induction method based on rough set models that more closely represents medical expert’s reasoning[J]. IEEE Engineering in Medicine and Biology, 2000(7-8):56-62.
[6] Mollestad T,Skowron A.A Rough set framework for data mining of prepositional default rules[A].In:The 9th International Symposium on Foundation of Intelligent System[C].Poland,1996.448-457.
[7] 尹旭日,陈世福.一种基于Rough集的缺省规则挖掘算法 [J].计算机研究与发展,2000,37(12):1441-1445.
　 Yin Xuri,Chen Shifu.An algorithm based on Rough set for mining default rules [J]. Journal of Computer Research & Development,2000,37(12):1441-1445.(in Chinese)
[8] Agrawal R,ImielinSki T,Swami A.Mining association rules between sets of items in large database[A].In: Proceeding of the ACM SIGMOD International Conference on Management of Data[C].Washington,DC,1993.207-216.
[9] Han J W,Pei J,Yin Y.Mining frequent patterns without candidate generation[A].In:Proceedings of the ACM SIGMOD International Conference on Management of Data[C].Dallas,USA,2000.1-12.
[10] Bayardo R J.Brute-force mining of high confidence classification rules[A].In: Proceedings of 3rd International Conference on Knowledge Discovery and Data Mining[C].Newport Beach,California,1997.123-126.
[11] Liu B,Hsu W,Ma Y M.Integrating classification and association rule mining[A].In:Proceedings of 4th Inter-national Conference on Knowledge Discovery and Data Mining[C].New York,1998.80-86.

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