期刊文献+

基于确信因子的有效关联规则挖掘 被引量:1

Mining Efficient Association Rules Based on CF Gene
在线阅读 下载PDF
导出
摘要 通过对现有的关联规则算法分析与研究发现,生成的关联规则具有相大的冗余性,且可能是无趣的,甚至是虚假的,为此人们主要提出了兴趣度作为有效规则评判标准。该文在先前研究的基础上,以确信因子为基础,提出确信度来使规则的有效性判断更加客观、合理。同时在算法中引入规则取舍,提高了挖掘有效规则的效率。 By analyzing and studying the most current algorithms about mining association rules,people find that gener-ated association rules are quite redundant ,and many rules,which possess high support and confidence are uninteresting,and even are false.Therefore,interest measurer is introduced to enchance the validity of association rules.Based on the previous work,this paper proposes CF measure and put s it as threshold to mine valuable rules.At the same time ,this paper establishes a theory architecture to accept or reject or reserve synchronously a pair of rules by analyzing CF gene,and introduces it to the algorithms.Finally,this algorithms is evaluated and is more efficient through experiments.
出处 《计算机工程与应用》 CSCD 北大核心 2004年第32期187-189,共3页 Computer Engineering and Applications
关键词 数据挖掘 关联规则 确信因子 data mining,association rules,CF gene
  • 相关文献

参考文献7

二级参考文献12

  • 1左万利 刘居正.包含正负属性的关联规则及其挖掘.第十六届全国数据库学术会议论文集[M].兰州,1999.288-292.
  • 2Han Jiawei Micheline Kamber.Data Mining:Concepts and Techniques[M].高等教育出版社,2001..
  • 31,Agrawal R, Mannila H, Srikant R et al. Fast discovery of association rules. In: Fayyad M, Piatetsky-Shapiro G, Smyth P eds. Advances in Knowledge Discovery and Data Mining. Menlo Park, California: AAAI/MIT Press, 1996. 307-328
  • 42,Brin S, Motwani R, Ullman J D et al. Dynamic itemset counting and implication rules for market basket data. In: Proc the ACM SIGMOD International Conference on Management of Data, Tucson, Arizon, 1997. 255-264
  • 53,Fayyad U M, Piatesky-shapiro G, Smyth P P. From data mining to knowledge discovery: an overview. In: Fayyad M, Piatetsky-Shapiro G, Smyth P eds. Advances in Knowledge Discovery and Data Mining. California:AAAI Press, 1996. 1-36
  • 64,Piatesket-Shapiro G. Discovery, analysis, and presentation of strong rules. In: Piatesky-Shapiro G, Frawley W J eds. Advances in Knowledge Discovery and Data Mining. Menlo Park, California:AAAI/MIT Press, 1991. 229-238
  • 75,Silberschatz A, Stonebraker M, Ullman J. What makes patterns interesting in knowledge discovery sysstems. IEEE Trans on Knowledge and Data Engineering, 1996, 8(6):970-974
  • 86,Symth P, Goodman R M. An information theoretic approach to rule induction from databases. IEEE Trans on Knowledge and Data Engineering, 1992, 4(4):301-316
  • 97,Toivonen H, Klemettinen M, Ronkainen P et al. Pruning and grouping discovered association rules. In: Mlnet Workshop on Statistics, Machine Learning, and Discovery in Database, Gete, Greece, 1995. 47-52
  • 10Aggarwal C C,Proc of the Int’ l Conf on Data Engineering,1998年,402页

共引文献143

同被引文献6

  • 1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
  • 2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
  • 3Lee W. A Data Mining Framework for Constructing Features and Models for Intrusion Detection Systems [D]. New York Columbia University, 1999.
  • 4Agrawal 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.
  • 5Agrawal R, Srikant R. Fast Algorithms for Mining Association Rules[R]. Reaeareh Re-port RJ9893, IBM Almaden Research Center, San Jose, California, 1994.
  • 6KDD Cup 1999 Data[EB/OL]. http://kdd. ics. uci. edu/databases/kddcup 99/kddcup99. html.

引证文献1

二级引证文献2

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部