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数据库中标准加权关联规则挖掘算法 被引量：2

Mining Algorithm of Normalized Weighted Association Rules in Database
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摘要 在原有的关联规则挖掘算法的研究中 ,认为所有的属性的重要程度相同 ,提出标准加权关联规则的挖掘算法 ,能够解决因属性重要程度不一样带来的问题。 Previous algorithms on mining association rules maintain that the importance of each item in database is equal. This paper presents a method of mining weighted association rules in database, which can solve the problems caused by the unequal importance of the items.
作者 杜鷁 藏海霞
机构地区 总参通信部网络技术管理中心 中国人民解放军
出处 《解放军理工大学学报（自然科学版）》 EI 2001年第2期9-12,共4页 Journal of PLA University of Science and Technology(Natural Science Edition)
基金 国家自然科学基金资助项目 ( 69975 0 2 4)
关键词 数据挖掘 关联规则 标准加权关联规则 数据库 关系数据库 data mining association rules normalized weighted association rules
分类号 TP311.132 [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5

  • 1AGRWAL R, IMIELINSKE T, SWAMI A. Mining association rules between sets of items in large databases [C]. In Proceedings of the ACM SIGMODInternational Conference on the Management of Data, Washington D C, 1993. 207-216.
  • 2AGRAWAL R, SRIKANT R. Fast algorithms for mining association rules [C]. In Proceedings of 20th International. Conference on Very Large Databases. Santiago, Chile, 1994. 487-488.
  • 3SAWASERE A, OMIECINSKI E, NAVATHE S. An efficient algorithm for mining association rules in large databases [C]. In Proceedings of 21thInternational conference on Very Large Databases, Zurich Switzerland, 1995. 432-443.
  • 4杜鹢 陆建江 宋自林.数据库中加权布尔型关联规则的挖掘[J].通信工程学院学报,1998,10(3):33-38.
  • 5CAI C H, ADA W C F, CHENG C H, et al. Miningassociation rules with weighted items [C]. In Proc of the International Database Engineering and Applications Symposium, Cardiff, Wales, U K, 1998. 68-77.

同被引文献14

  • 1Savasere A, Omiecinski E, Navathe S. An efficient algorithm for mining association rules in large databases [C]//Proceedings of the 21st VLDB Conference. Zurich, Switzerland, 1995 : 254-262.
  • 2Hipp J, Untzer U G, Nakhaeizadeh G. Algorithms for association rule mining-a general survey and comparison[J]. SIGKDD Explorations, 2000, 2(2):1-58.
  • 3John D H, Soon M C. Mining association rules using inverted hashing and pruning [J]. Information Processing Letters,2002,83 (4) :211-220.
  • 4Chen Guoqing, Wei Qiang, Liu De, et al. Simple association rules (SAR)and the SAR-based rule discovery [J]. Computers and Industrial Engineering, 2002,43 ( 4 ) : 721-733.
  • 5Wang Wei, Yang Jiong. Efficient mining of weighted association rules[ C ]//Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Edmonton, Canada, 2002: 270-274.
  • 6Cai C H, Ada W C, Cheng C H, et al. Mining association rules with weighted items [ C ]// Proceedings of the International Database Engineering and Applications Symposium.Wales, UK, 1998: 68-77.
  • 7Murtagh F, Farid M. Weighted association rule mining using weighted support and significance framework [ C ]//Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Washington DC,USA, 2003 : 661-666.
  • 8Agrawal R, Imielinski T, Swami A. Mining association rules between sets of items in large databases [C]//Proceedings of the ACM SIGMOD Conference on Management of Data. Washington DC, USA, 1993:207-216.
  • 9Agrawal R, Srikant R. Fast algorithms for mining association rules[ C]//Proceedings of the 20th International Conference on Very Large Databases. Santiago, Chile, 1994:487-499.
  • 10陈祖琴,张惠玲,葛继科,郑宏.基于加权关联规则挖掘的相关文献推荐[J].现代图书情报技术,2007(10):57-61. 被引量：14

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二级引证文献14

解放军理工大学学报（自然科学版）
2001年 第2期

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