期刊文献+

基于区分矩阵的属性约简算法改进策略 被引量:4

Improvement of attribute reduction algorithm based on discernibility matrix
下载PDF
导出
摘要 针对大容量数据表构造的区分矩阵过于庞大致使属性约简算法效率低的问题,引入置信度和支持度,提取大型数据库中的高概率事件,重新构造决策数据表,并在构造区分矩阵过程中剔除重复项和包含项,结果使得比较次数减少、存储空间节省、约简效率提高。 In knowledge system of large database,large discernibility matrix reduces efficiency of attribute reduction algorithm.To solve this problem,confidence and support are introduced to reconstruct the decision table by extracting high probability events of a large database.In the reduction algorithm based on discernibility matrix attribute,the duplicates and contains items are removed to reduce comparison times.As a result,the efficiency of attribute reduction algorithm is improved and the storage space is saved.
出处 《武汉科技大学学报》 CAS 2011年第2期126-130,共5页 Journal of Wuhan University of Science and Technology
基金 国家高技术研究发展计划(863计划)资助课题(2009AA04Z136)
关键词 决策表 区分矩阵 属性频度 属性约简 decision table discernibility matrix attribute frequency attribute reduction
  • 引文网络
  • 相关文献

参考文献12

  • 1Pawlak Z. Rough sets [J]. International Journal of Computer and Information Science, 1982, 11 (5): 341-356.
  • 2Skowron A, Rauszzer C. The discernbility matrices and functions in information systems [M]. Dordrecht: Kluuer Academic Publishers, 1992: 331- 362.
  • 3Zhangyan Xu, Liyu Huang. Bingru Yang. Efficient attribute reduction algorithm based on Skowron discernibility matrix[C] // 2009 International Workshop on Intelligent Systems and Applications (ISA): International Workshop on Intelligent Systems and Applications. China, Wuhan: Institute of Electrical and Electronics Engineers, Inc, 2009 : 1-4.
  • 4Shifei Ding, Hao Ding. Research and development of attribute reduction algorithm based on rough set [C]// The 2010 Chinese Control and Decision Conference :2010 Chinese Control and Decision Conference (CCDC 2010). China, Xuzhou: Institute of Electrical and Electronics Engineers,Ine, 2010:648 - 653.
  • 5程昳.多元粗糙模糊回归预测[J].四川师范大学学报(自然科学版),2003,26(4):335-337. 被引量:69
  • 6刘文军,谷云东,李洪兴.基于区分矩阵求决策算法的约简[J].北京师范大学学报(自然科学版),2003,39(3):311-315. 被引量:18
  • 7张健,王蔚.基于支持度与置信度阈值优化技术的关联分类算法[J].计算机应用,2007,27(12):3032-3034. 被引量:9
  • 8Keyun Hu, Yuefei Sui, Ju Wang,et al. Rough set theory under similarity relation [C]// The 5th World Multiconference on Systemics, Cybernetics and Informatics 2001, Orlando, Florida (USA) : The International Institute of Informatics and Systemics, 2001(1) :405-409.
  • 9叶东毅,陈昭炯.一个新的差别矩阵及其求核方法[J].电子学报,2002,30(7):1086-1088. 被引量:243
  • 10Pawlak Z, Skowron A. Rough sets and Boolean reasoning [J]. Information Sciences, 2007,177 ( 1 ) 41-73.

二级参考文献24

  • 1李洪兴.因素空间理论与知识表示的数学框架(Ⅶ)──多重目标综合决策[J].模糊系统与数学,1995,9(2):16-24. 被引量:26
  • 2李洪兴.因素空间理论与知识表示的数学框架(Ⅰ)──因素空间的公理化定义与描述架[J].北京师范大学学报(自然科学版),1996,32(4):470-475. 被引量:67
  • 3曾黄麟.粗集理论及其应用[M].重庆:重庆大学出版社,1998..
  • 4山东省农作物病虫测报站.农业病虫数理统计预报[M].济南:山东科学技术出版社,1982..
  • 5LIU B, HSU W, MAY. Integrating classification and association rule mining [C]// Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining ( KDD-98). New York: AAAI, 1998: 80 - 86.
  • 6LI W M, HAN J W, PEI J. CMAR: accurate and efficient classification based on multiple class-association rules [C]//Proceedings of the 1st IEEE International Conference on Data Mining (ICDM 2001 ). Washington: IEEE Computer Society, 2001: 369- 376.
  • 7YIN X X, HAN J W. CPAR: classification based on predictive association rules [C]// Proceedings of the 2003 SIAM International Conference on Data Mining (SDM'03). San Francisco: [ s. n.], 2003:34 - 42.
  • 8COENEN F, LENG P. Data structures for association rule mining: t-trees and p-trees [J]. IEEE Transaction in KnowIedge and Data Engineering, 2004, 16(6) : 774 -778.
  • 9COENEN F. LUCS-KDD DN Software (Version 2) [ EB/OL]. [2007 -04 -02]. http://www. csc. liv. ac. uk/- frans/KDD/Software/ LUCS_KDD_DN/.
  • 10AGRAWAL R, SRIKANT R. Fast algorithms for mining association rules [C]// Proceeding of the 1994 International Conference on Vary Large Data Bases. Santiago: Morgan Kaufmann, 1994:487 -499.

共引文献342

同被引文献46

引证文献4

二级引证文献39

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