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基于参考度的有效关联规则挖掘 被引量：2

Mining Efficient Association Rules Based on Consult
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摘要 针对当前关联规则挖掘采用的支持度-置信度框架在具体应用中存在的问题,引入新的指标——参考度对模型中关联规则挖掘算法评价体系进行改进。实验表明,通过引入参考度,能够提高挖掘有效规则的效率。 People find that generated association rules may be false or redundant when using the traditional evaluating indicator.The article introduces the new measure-consult to improve measure system of mining algorithm.The experiments show that the efficiency of valid rules can be enhanced by adding the new measure.
作者 柳景超 宋胜锋
机构地区 海军工程大学电子工程学院
出处 《火力与指挥控制》 CSCD 北大核心 2011年第5期79-81,共3页 Fire Control & Command Control
基金 湖北省自然科学基金资助项目(2006ABA009)
关键词 数据挖掘 关联规则 入侵检测 参考度 data mining association rules intrusion detection measure-consult
分类号 TP393.08 [自动化与计算机技术—计算机应用技术] TP311.13 [自动化与计算机技术—计算机软件与理论]
  • 相关文献

参考文献7

  • 1Lee W. A Data Mining Framework for Constructing Features and Models for Intrusion Detection Systems [D]. New York Columbia University, 1999.
  • 2Agrawal R, Imielinske T, Swami A. Mining Association Rules Between Sets of Items in Large Databases [C]//Proc. of the ACM SIGMOD International Conference on the Management of Data. Washington D.C, 1993, 5: 207-216.
  • 3Agrawal R, Srikant R. Fast Algorithms for Mining Association Rules[R]. Reaeareh Re-port RJ9893, IBM Almaden Research Center, San Jose, California, 1994.
  • 4伊卫国,卫金茂,王名扬.挖掘有效的关联规则[J].计算机工程与科学,2005,27(7):91-94. 被引量：9
  • 5吴良杰,刘红祥,况振东.基于确信因子的有效关联规则挖掘[J].计算机工程与应用,2004,40(32):187-189. 被引量：1
  • 6罗可,郗东妹.采掘有效的关联规则[J].小型微型计算机系统,2005,26(8):1374-1379. 被引量：12
  • 7KDD Cup 1999 Data[EB/OL]. http://kdd. ics. uci. edu/databases/kddcup 99/kddcup99. html.

二级参考文献23

  • 1Agrawal R et al. Mining association rules between sets of items in large databases[C]. In: Proceedings of ACM SIGMOD Conference on Management of Data, Washington DC. 1993, 207-216.
  • 2Agrawal R, Srikant R. Fast algorithms for mining association rules[C]. In: Proceedings of the 20th International Conference on Very Large Databases, Santiago, Chile, 1994, 487-499.
  • 3Agrawal R et al. Fast discovery of association rules[M]. In:Advances in Knowledge Discovery and Data Mining, AAAI/MIT Press,Menlo Park, California ,1996, 307-328.
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  • 7Savasere A, Omiecinski E, Navathe S. Mining for strong negative association in a large database of customer transactions[C].In: Proceedings of the International Conference on Data Engineering. Orlando, Florida. 1998. 494-502.
  • 8Ahmed K M, E1-Makky N M, Taha Y. A note on “Beyond market basket: Generalizing association rules to correlations[C]. ” SIGKDD Explorations 1,2000,46-48.
  • 9R Agrawal, T Imielinski, A Swami.Mining Association Rules Between Sets of Items in Large Databases [A].Proc 1993 ACM SIGMOD Conf on Management of Data[C].1993.207-216.
  • 10http://www.ics.uci.edu/～mlearn/MLSummary.html,2003-05.

共引文献19

同被引文献9

引证文献2

二级引证文献1

火力与指挥控制
2011年 第5期

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