一种基于用户指导的多关系关联规则挖掘算法
被引量：1
A Multi-Relational Association Rule Mining Algorithm with User's Guidance
摘要
提出一种基于用户指导的多关系关联规则挖掘算法,对传统的关联规则挖掘方法进行拓展,借鉴元组ID传播的思想使多表间无需物理连接而能直接进行关联规则挖掘,并引入了用户指导的概念,提高了用户的满意程度及挖掘的效率和精确度.该算法能够直接支持关系数据库,且运行时间远远小于基于ILP技术的多关系关联规则挖掘算法.
出处
《计算机研究与发展》
EI
CSCD
北大核心
2007年第z2期22-26,共5页
Journal of Computer Research and Development
基金
国家自然科学基金项目(60673136)
参考文献4
-
1[1]Saso Dzeroski,N Lavrac.Relational Data Mining.Berlin:Springer,2001
-
2[2]N Lavrac,S Dzeroski.Inductive Logic Programming:Techniques and Applications.New York:Ellis Horwood,1994
-
3[3]L Dehaspe,H Toivonen.Discovery of relational association rules.In:S Dzeroski,N Lavrac,eds.Relational Data Mining,Berlin:Springer,2001.189-212
-
4[4]Xiaoxin Yin,Jiawei Han,Jiong Yang,et al.CrossMine:Efficient classification across multiple database relations.The 20th Int'lConf on Data Engineering (ICDE'04),Boston,MA,2004
同被引文献10
-
1DZEROSKI S, LAVRAC N. Relational data mining[ M]. Berlin: Springer ,2001.
-
2AGRAWAL R, SRIKANT R. Fast algorithms for mining association rules[ C]//Proc of International Conference on Very Large Data Bases. Santiago:[ s. n. ], 1994:487-499.
-
3KAMBER M, HAN Jia-wei, CHIANG j Y. Metarule-guided mining of multi-dimensional association rules using data cubes [ C ]//Proc of International Conference on Knowledge Discovery and Data Ming, Newport Beach: [ s. n. ] , 1997:207-210.
-
4LAVRAC N, DZEROSKI S. Inductive logic programming:techniques and applications[ M ]. New York : Ellis Horwood, 1994.
-
5DEHASPE L. Mining association rules in multiple relations[ C ]// Proc of the 7th International Workshop on Inductive Logic Programming. Berlin : Springer, 1997 : 125-132.
-
6DEHAPE L. Frequent pattern discovery in first-order logic[ D]. Belgium : Katholieke University Leuven, 1998.
-
7NIJSSEN S, KOK J. Faster association rules for multiple relations [ C ]//Proc of the 17th International Conference on Artificial Intelligence. 2001:891-896.
-
8YIN Xiao-xin, HAN Jia-wei, YANG Jiong, et al. CrossMine: efficient classification across multiple database relations [ C ]//Proc of International Conference on Data Engineering. Boston : [ s. n. ], 2004 : 399-410.
-
9YIN Xiao-xin, HAN Jia-wei, YU P S. Cross-relational clustering with user' s guidance[ C]//Proc of the llth ACM SIGKDD Conference on Knowledge Discovery in Data Mining. Chicago: [ s. n. ], 2005.
-
10HANJia-wei,KAMBERM.数据挖掘:概念与技术[M].范明.孟小峰,译.2版.北京:机械工业出版社.2007:164-165.
-
1郭景峰,吕庆春,李霞.一种新型基于用户指导的多关系关联规则挖掘算法[J].微计算机信息,2009,25(24):124-126. 被引量：2
-
2侯伟,杨炳儒,宋威.多关系关联规则算法综述[J].计算机工程与应用,2007,43(23):1-5. 被引量：3
-
3何军,刘红岩,杜小勇.挖掘多关系关联规则[J].软件学报,2007,18(11):2752-2765. 被引量：37
-
4邓左祥,刘连芳,梁一平,周小平.一种多关系频繁模式挖掘算法[J].计算机应用研究,2009,26(9):3285-3288. 被引量：1
-
5那罡,石菲,董丽凤,边凯.科技创新ON之路[J].中国计算机用户,2007(13):47-53.
-
6张晓东,王斌.浅谈数据挖掘和自适应算法的应用[J].电脑知识与技术,2016,12(10):23-23.
-
7左向科,邢永康,王嵘.关联规则挖掘算法及优化策略研究[J].微处理机,2009,30(5):101-104.
-
8邓左祥,李春贵.一种有效的多关系聚类算法[J].微电子学与计算机,2016,33(4):133-137. 被引量：1
-
9舒风笛,毋国庆,王敏.图象数据关联规则挖掘[J].小型微型计算机系统,2001,22(11):1353-1356. 被引量：23
-
10徐光美,刘宏哲,张敬尊.基于特征加权的多关系朴素贝叶斯分类模型[J].计算机科学,2014,41(10):283-285. 被引量：12
