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改进的关联规则算法及其应用 被引量:10

Improved Association Rule Algorithm and Its Application
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摘要 本文根据数据挖掘中关联规则的性质以及高校成绩管理数据库的自身特点,在经典关联规则算法Apriori算法的基础上提出了一种改进的算法A++算法,并利用该算法对学生成绩管理数据库进行了关联规则挖掘,得到了隐含在数据库中的有用信息。
出处 《计算机系统应用》 2007年第10期80-84,共5页 Computer Systems & Applications
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参考文献11

  • 1唐常杰,杨富华,杨璐.数据采掘的基本方法及其与专家系统的差异[J].计算机应用,1999,19(3):17-20. 被引量:11
  • 2Agrawal 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.New York ACM,1993:207-216.
  • 3Agrawal R,SriKant R.Fast algorithms for mining association rules[C].Proceedings of the 20th International Conference on Very Large Database.[s.l.]:Morgan Kaufman Pub Inc,1994:487 -499.
  • 4Park J S,Chen M S,Yu P S.An effective hashbased algorithm for mining association rules[C].Proceedings of the ACM SIGMOD International Conference on Management of Data.New York ACM,1995:175 -186.
  • 5Park J S,Chen M S,Yu P S.Efficient parallel data mining of association rules[C].Proceedings of the 4th International Conference on Information and Knowledge Management New York ACM.1995:31-36.
  • 6Savasere A,Omiecinski E,Navathe S.An efficient algorithm for mining association rules in large databases[C].Proceedings of the 21st International Conference on Very Large Database.New York ACM,1995:432 -443.
  • 7Toivonen H.Sampling large databases for association rules[C].Proceedings of the 22nd International Conference on Very Large Database.Bombay,India[s.n.],1996:134 -145.
  • 8Brin S,Motwani R,Ullman J D,etc.Dynamic Itemset Counting and Implication Rules for Market Basked Data[C].Proceedings of the ACM SIGMOD International Conference on Management of Data.New York ACM,1997:255 -264.
  • 9惠晓滨,张凤鸣,虞健飞,牛世民.一种基于栈变换的高效关联规则挖掘算法[J].计算机研究与发展,2003,40(2):330-335. 被引量:15
  • 10曾万聃,周绪波,戴勃,常桂然,李春平.关联规则挖掘的矩阵算法[J].计算机工程,2006,32(2):45-47. 被引量:33

二级参考文献30

  • 1王创新.关联规则提取中对Apriori算法的一种改进[J].计算机工程与应用,2004,40(34):183-185. 被引量:32
  • 2李清峰,杨路明,张晓峰,龙艳军.数据挖掘中关联规则的一种高效Apriori算法[J].计算机应用与软件,2004,21(12):84-86. 被引量:29
  • 3徐章艳,刘美玲,张师超,卢景丽,区玉明.Apriori算法的三种优化方法[J].计算机工程与应用,2004,40(36):190-192. 被引量:71
  • 4唐常杰 张天庆 等.基于时态数据库的Web数据周期性的发现.全国第15届数据库论文集[M].,..
  • 5Jawei,KDD’96,1996年
  • 6唐常杰,全国第15届数据库论文集
  • 7Agarwal R, Imielinski T, Swami A. Mining Associaiton Rules Between Sots of Items in Large Databttscs [A]. In: Proceeding of 1993 SIGMOD International Conterence on Management of Data [C].New York: ACM Press, 1993:207-216.
  • 8Park J S, Chen M S, Yu P S. Using a Hash-based Method with Transaction Trimming for Mining Associations Rules[J]. IEEE Transactions on Knowledge and Data Engineering, 1997, (9):813-825.
  • 9Agarwal R, Agarwal C. A Tree Projection Algorithm for Generation of Frequent Itemsets[J]. Journal of Parallel and Distributed Compuling,2001, (Special Issue on High Performance Data Mining): 1-23.
  • 10Han Jiawei, Pei Ham Yin Yiwen. Mining Frequent Patterns Without Candidate Generation[A]. In: Proceeding of 2000 ACM SIGMOID International Conference on Management of Data [C]. New York:ACM Press, 2000: 1-12.

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