期刊文献+

粗集理论及其在数据挖掘中的应用 被引量:2

Rough sets theory and its application to data mining
下载PDF
导出
摘要 粗集理论是一种新的处理模糊和不确定性的数学工具,在数据挖掘、机器学习、决策支持系统和模式识别等领域得到了广泛应用.文中在阐述了粗集理论的基本思想与特点的基础上,重点评述了粗集理论在数据挖掘中的应用状况以及未来的发展方向. Rough sets theory is a new mathematical means to deal with vagueness and uncertainty. It has been used in many fields such as data mining, machine learning, decision support systems and pattern recognition. This paper explains the basic idea and characteristics of rough sets theory, mainly reviewing the applicable status in data mining and its research direction in the future.
作者 管宝云
出处 《天津工业大学学报》 CAS 2002年第6期29-31,共3页 Journal of Tiangong University
关键词 粗集理论 数据挖掘 数据约简 规则抽取 rough sets theory data mining data reduction rule extraction
  • 相关文献

参考文献11

  • 1Pawlak Z, Busse J G. Rough sets[J]. Communications on the ACM, 1995,38(11) :89 -95.
  • 2Chen M S, Han J. Data mining:An overview from a database perspective [J]. IEEE Trans on Knowledge and Data Engineering, 1996,8 (6) :866 - 883.
  • 3Ziarko W. Introduction to the special issue on rough sets and knowledge discovery [J]. Computational Intelligence, 1995,11 (2) :223 -226.
  • 4Ziarko W. Variable precision rough set model[J]. Journal of Computer and System Sciences, 1993,46 ( 1 ): 39 - 59.
  • 5Quafafou M. α-RST:a generalization of rough set theory[J].Information Sciences,2000,124 (4) :301 - 316.
  • 6Jelonek J,Krawiec K. Rough set reduction of attributes and their domains for neural networks [J]. Computational Intelligence, 1995,11 (2) :339 - 347.
  • 7Kryszkiewicz M. Rules in incomplete systems [J]. Information Sciences, 1999,113 (4): 271 - 292.
  • 8Hu Xiaohua, Cercone N. Learning maximal generalized decision rules via discretization, generalization and rough set feature selection [A]. Ninth IEEE International Conference on Tools with Artificial Intelligence [C]. Chicago: Newport Beach, 1997. 548 - 556.
  • 9Chen Xianghui, Zhu Shanjun, Ji Yindong. Entropy based uncertainty measures for classification rules with inconsistency tolerance [A]. IEEE International Conference on Systems, Man, and Cybernetics [C]. Nashville, 2000,2816 -2821.
  • 10Zhong Ning, Dong Ju-Zhen. An incremental, probabilistic rough set approach to rule discovery [A]. The 1998 IEEE International Conference on Fuzzy Systems Proceedings[C]. Anchorage, 1998,933 - 938.

同被引文献15

  • 1王福保.概率论与数理统计[M].上海:同济大学出版社,1994.164-182.
  • 2Andrienko G,Andrienko N.Data mining with C4.5 and cartographic visualization[C]∥User Interfaces to Data Intensive Systems.IEEE Computer Society,1999:162-165.
  • 3Pawlak Z,Busse J G.Rough sets[J].Communications of the ACM,1995,38(11):89-95.
  • 4Tsumoto S.Automated extraction of medical expert system rules from clinical database based on rough set theory[J].Information Sciences,1998,112(1):67-84.
  • 5Pawlak Z. Rough sets [J]. Int. Journal of Computer and information Sciences, 1982, 11:341 ~ 356.
  • 6Pawlak Z, Busse JG.. Rough sets [J]. Communications on the ACM, 1995, 38 (11): 89~95.
  • 7Pawlak Z. Rough set theory and its applictions to data analysis [ J ].Cyberneticsand Syst, 1998, 29 (7): 661688.
  • 8Ziakow. Variable precision rough sets model [J]. Journal of Computer and Systems Sciences, 1993, 46 (1): 39 ~ 59.
  • 9HuX. Knowledge Discovery in Databases: An Attribute-oriented Rough Set Approach [D]. University of Regina, Canada, 1995.
  • 10顾冲时 郑东健 胡群革.陈村水电站原型观测资料及结构正反分析报告[R].河海大学,2002..

引证文献2

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