期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

基于元学习的分布式挖掘频繁闭合模式算法研究 被引量：1

Study of algorithm for distributed mining frequent closed patterns based on meta-learn technology
下载PDF
导出
摘要 利用元学习技术提出了一种分布式挖掘频繁闭合模式算法;为适应不同的分布式环境,还给出了该算法的一个变种;最后通过实验讨论了不同分布式下选取算法的策略。算法具有挖掘效率高、通信量少、可靠性高的特点,适合分布式挖掘。 This paper presented a distributed algorithm for mining frequent closed patterns using recta-learning. In order to accommodate the different distributed environment, also presented another similar algorithm. In the end, discussed the strategy for choosing the right distributed algorithm by experiment. This algorithm is more efficient and has less communicated and high reliability, applicable to distributed mining well.
作者 琚春华 倪栋君
机构地区 浙江工商大学计算机与信息工程学院
出处 《计算机应用研究》 CSCD 北大核心 2009年第1期41-43,46,共4页 Application Research of Computers
基金 国家自然科学基金资助项目(70671094) 国家社会科学基金资助项目(05BTJ019) 浙江社科规划课题(06CGGL29YBB) 浙江科技计划资助项目(2007C24004) 国家博士学科点专项科研基金资助项目(20050353003)
关键词 数据挖掘 频繁闭合模式 分布式挖掘 元学习 data mining frequent closed patterns distributed mining meta-learning
分类号 TP301.6 [自动化与计算机技术—计算机系统结构]
  • 相关文献

参考文献9

  • 1AGRAWAL R, SRIKANT R. Fast algorithms for mining association rules[ C ]//Proc of the 20th International Conference on very Large Databases. Santiago : Morgan Kaufmann Publishers, 1994:487-499.
  • 2PASQUIER N, BASTIDE Y, TAOUIL R, et al. Discovering frequent closed itemsets for association rules [ C ]//Proc of the 7th International Conference on Database Theory. London: Springer-Verlag, 1999: 398-416.
  • 3WANG Jian-yong, HAN Jia-wei, PEI Jian. CLOSET + : searching for the best strategies for mining frequent closed itemsets [ C ]//Proc of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM Press, 2003:236-245.
  • 4ZAKI M J, HSIAO C J. CHARM: an efficient algorithm for closed itemset mi-ning[ C ]//Proc of the 2nd SIAM International Conference on Data Mining. Arlington : SIAM ,2002 : 12-28.
  • 5缪裕青.频繁闭合项目集的并行挖掘算法研究[J].计算机科学,2004,31(5):166-168. 被引量：5
  • 6CHAN P. An extensible meta-learning approach for scalable and accurate inductive learning [ D ]. New York: Columbia University, 1996.
  • 7张继福,郑链,史虹,姚淑萍.基于元学习技术的分布式采掘关联规则研究[J].系统仿真学报,2004,16(4):696-699. 被引量：3
  • 8王春花,黄厚宽.利用抽样技术分布式开采可变精度的关联规则[J].计算机研究与发展,2000,37(9):1101-1106. 被引量：12
  • 9SRIKANT R. Quest synthetic data generation code [ R ]. San Jose : IBM Almaden Research Center, 1994.

二级参考文献22

  • 1[1]Agrawal R, et al. Mining association rules between sets of items in large databases. In: Proc. 1993 ACM SIGMOD Int'l Conf. Management of Data,Washington,D. C. ,May 1993
  • 2[2]Agrawal R,et al. Fast algorithms for mining association rules. In:Proc. 1994 VLDB Int'l Conf. ,Santiago,Chile,Sept. 1994
  • 3[3]Han Jiawei, et al. Mining frequent patterns without candidate generation. In:Proc. ACM SIGMOD Int'l Conf. On Management of Data,Dallas ,Texas ,May 2000
  • 4[4]Pasquier N, et al. Discovering frequent closed itemsets for association rules. In: Proc. Int'l Conf. On Database Theory,Jan. 1999
  • 5[5]Wang Jianyong, et al. CLOSET+: searching for the best strategies for mining frequent closed itemsets. In: Proc. Ninth ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining,Washington,DC,Aug. 2003
  • 6[6]Agrawal R, et al. Parallel mining of association rules. IEEE Trans. on Knowledge and Data Eng. ,Dec. 1996.8
  • 7[7]Han Eui-Hong, et al. Scalable parallel data mining for association rules. IEEE Trans. on Knowledge and Data Eng., May/June 2000,12
  • 8[8]Zalane O R, et al. Fast parallel association rule mining without candidacy generation. In:Proc. IEEE Int'l Conf. On Data Mining,San Jose ,CA USA, Nov. 2001
  • 9Park J S，Proc of the Fourth Int’ l Conf on Knowledge Discovery andData Mining，1998年
  • 10Chan P，Ph D dissertation，1996年

共引文献17

同被引文献5

引证文献1

计算机应用研究
2009年 第1期

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