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多关系数据库中的关联规则挖掘 被引量：3

Mining of Association Rules in the Multi-Relation Database
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摘要 频繁模式发现是数据挖掘的重要任务之一,现有的关联规则挖掘方法或者是面向单关系,或者是面向一些较为简单的模型,本文在结合原有一些方法的同时,通过给出一些新的概念,提出了在更复杂关系模型中的挖掘方法,给出算法,并对算法进行了分析。 Frequent pattern found is one of the most important task in data mining, but the existing methods for mining of association rules are only single relationship-oriented or for some relatively simple models. This paper presents a new method that is effective for more complex model of relations.The algorithm of the method is also given.
作者 魏欣楠 郝忠孝
机构地区 哈尔滨理工大学计算机科学与技术学院
出处 《自动化技术与应用》 2009年第3期41-43,共3页 Techniques of Automation and Applications
关键词 多关系数据库 关联规则 频繁模式 数据挖掘 multi-relation database association rule
分类号 TP311.131 [自动化与计算机技术—计算机软件与理论]
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参考文献9

  • 1AGRAWAL R,SRIKANT R.Fast algorithms for mining association roles in large databases[C].Bocca JB,Jarke M,Zaniolo C,eds,Proc.of the 20th In'l Conf.on Very Large Data Bases. Santiago:Morgan Kaufmann, 1994.478-499.
  • 2HIPP J,GUNTZER U,NAKAEIZADEH G. Algorithms for Assiciation Rule Mining A General Survey and Comparison[C]. In: Proc ACM SIGKDD Explorations,vol. 2, Issue 1,pp. 58-63,2000.
  • 3L.DE RAEDT,and S.WROBEL,EDITORSo Proceedings of the First International Workshop on MultiRelational Data Mining,S. Dzeroski[C].KDD-2002:Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,Edmonton, Canada,2002.
  • 4SIEGFRIED NIJSSEN and JOOST KOK.Faster Association Rules for Multiple Relations[R].Leiden Institute of Advanced Computer Science, Leiden University.Proc. of the 17th Intl Joint Conl.on Artificial Intelligence,Morgan Kaufmann Publishers,Inc, Seattle, USA, 2001.891-896.
  • 5ERIE KA KA NG,ADA WAI-CHEE FU,KE WANG. Mining Association Rules from Stars[R]. Chinese University of Hong Kong Department of Computer Science and Engineering. Proc.of the 2002 IEEE Int'l Conf.on Data Mining(ICDM 2002). Los Alamitos: IEEE Computer Society, 2002.322-329.
  • 6SASO DZEROSKI S.Multi-Relational Data Mining:An Introduction. Jozef Stefan Institute, Jamova 39, SI- 1000 Ljubljana, Slovenia. SIGKDD Explorations, 2003,5(1):1-16.
  • 7张伟,杨炳儒,钱榕.多关系频繁模式发现研究[J].计算机科学,2007,34(7):158-164. 被引量：3
  • 8AMANDA CLARE, HUGH E. WILLIAMS. Scalable Multi-Relational Association Mining[R].Department of Computer Science, University of Wales Aberystwyth Proceedings of the 4th IEEE International Conference on Data Mining,Brighton,2004:355-358.
  • 9Xu LI-JUN,XIE KANG-LIN.A novel algorithm for frequent itemset mining in data warehouses.Department of Computer Science and Engineering,Shanghai Jiao Tong University[J]. Journal of Zhejiang University,2006,7(2):216-224.

二级参考文献45

  • 1Lavra c N, D zeroski S. Inductive Logic Programming: Techniques and Applications. Ellis Horwood, Chichester, 1994
  • 2De Raedt L. Logical settings for concept-learning. Artificial Intelligence, 1997,95(1)
  • 3Muggleton S,De Raedt L. Inductive logic programming : Theory and methods. Journal of Logic Programming, 1994,19,20 : 629- 679
  • 4Dehaspe L,De Raedt L. Mining association rules in multiple relations. In:Proc. of the 7th Intl Workshop on Inductive Logic Programming, vol 1297 of Lecture Notes in Artificial Intelligence, 1997
  • 5Dehaspe L,Toivonen H. Discovery of Frequent Datalog Patterns. Data Mining and Knowledge Discovery, 1999, 3(1):7-36
  • 6Dehaspe L,Toivonen H,Kind RD. Finding frequent substructures in chemical compounds. In:Proc. of the KDD-98, 1998
  • 7Mannila H,Toivonen H. Levelwise search and borders ot theories in knowledge discovery. Data Mining and Knowledge Discovery , 1997, 1(3) :241-258
  • 8Ullman J D. Principles of Database and Knowledge-Base Systems. volume Ⅰ. Rockville, MD: Computer Science Press, 1988
  • 9Nedellec C, Ade H, Bergadano F, et al. Declarative bias in ILP. In:De Raedt L, ed. Advances in Inductive Logic Programming, volume 32 of Frontiers in Artificial Intelligence and Applications, IOS Press, 1996. 82-103
  • 10Blockeel H,Raedt L D. Top-Down Induction of First-order Logical Decision Trees. Artificial Intelligence, 1998, 101 (1-2) : 285- 297

共引文献2

同被引文献29

  • 1陈华平,唐军.移动支付的使用者与使用行为研究[J].管理科学,2006,19(6):48-55. 被引量：81
  • 2艾瑞市场咨询.中国移动增值服务市场研究报告[Z].2008.
  • 3赛迪顾问咨询公司.2008-2009年中国B2C电子商务市场研究年度报告[Z].北京:赛迪顾问咨询公司,2009.
  • 4Ostrom A, lacobucci D. Consumer trade-offs and the evaluation of services [J].Journal of Marketing, 1999,59:17-28.
  • 5Agrawal R, et al. Mining association rules between sets of items in large databases[C]// Proceedings of the 1993 ACM-SIGMOD Conference on Management of Data, 1993 : 207- 216.
  • 6Agrawal R,Skirant R. Fast algorithms for mining association rules in large databases[Z]. California: IBM Almaden Research Center, 1994 : 28- 36.
  • 7WANG Xing-ping. The influence of parameters and system to turbo-unit operation efficiency[J]. Power Engineering, 2007, 17(3), 48-52.
  • 8AGRAWAL R, IMIELINSKI T; SWAMI A. Mining associa- tions between sets of items in massive databases[A]. In Proc of the ACM-SIGMOD 1993 International Conference on Man- agement of Data[C]. Washington:[s. n. ], 1993. 207-216.
  • 9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：38
  • 10胡秋灵,孙权.运用整合科技接受模型对消费者移动支付使用意愿的解析[J].统计与决策,2008,24(6):119-121. 被引量：15

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自动化技术与应用
2009年 第3期

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