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面向领域的数据驱动自主式知识获取模型及实现 被引量:4

Domain-oriented data-driven knowledge acquisition model and its implementation
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摘要 数据挖掘是一个非常有用的工具,通过它能够从大型数据库中发现知识。目前,众多研究者将其主要研究工作放在了数据挖掘的模型与方法等工程技术问题上,对于数据挖掘的一些基础理论问题却研究不足。通过对数据挖掘基本理论的研究,提出了面向领域的数据驱动自主式知识获取模型。并通过一系列数据驱动自主式知识获取算法验证了该模型的有效性。 Data mining technology is a useful tool fnr knowledge discovery from large-scale databases. At present, most data mining researchers pay much attention to technical problems for developing data mining models and methods, while little to basic issues of data mining. Based on the study of the basic theory of data mining, a domain-oriented data-driven knowledge acquisition model was proposed, and the validity of this model was verified by the data-driven data mining algorithms.
出处 《重庆邮电大学学报(自然科学版)》 北大核心 2009年第4期502-506,共5页 Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition)
基金 国家自然科学基金项目(60773113 60573068) 重庆市自然科学基金项目(2008BA2017 2008BA2041) 甘肃省教育厅硕导基金(0803-07)
关键词 面向领域 数据驱动 数据挖掘 知识获取 domain-oriented data-driven data mining knowledge acquisition
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  • 1王国胤.决策信息系统中的不确定性度量[J].计算机科学,2001,28(5):23-26.
  • 2Duntsch I, Gediga G. Uncertainty Measurement of Rough Set Prediction. Artificial Intelligence, 1998, 106(1): 109-137.
  • 3Yin Desheng, Wang Guoyin, Wu Yu. A Self-Learning Algorithm for Decision Tree Pre-Pruning// Proc of the 3rd International Conference on Machine Learning and Cybernetics. Shanghai, China, 2004:2140-2145.
  • 4Ganter B, Wille R. Formal Concept Analysis. New York, USA: Springer-Verlag, 1999.
  • 5Fu Huaiyu, Fu Huaiguo, Njiwoua P, et al. A Comparative Study of FCA-Based Supervised Classification Algorithms // Proe of the 2nd International Conference on Formal Coneept Analysis. Sydney, Australia, 2004:313-320.
  • 6Carpineto C, Romano G. Galois: An Order-Theoretic Approach to Conceptual Clustering// Proc of the 10th International Conference on Machine Learning. Amherst, USA, 1993:33-40.
  • 7Sahami M. Learning Classification Rules Using Lattices // Proc of the 8th European Conference on Machine Learning. Heraclion, Greece, 1995:343-346.
  • 8Mephu-Nguifo E. Galois Lattice: A Framework for Concept Learning, Design, Evaluation and Refinement//Proc of the 6th International Conference on Tools with Artificial Intelligence. New Orleans, USA, 1994:461-467.
  • 9Xie Z P, Liu Z T. Research on Classifier Based on Lattice Structure// Proc of the Conference on Intelligent Information Processing. Beijing, China, 2000, 333-338.
  • 10Anamika G, Naveen K, Vasudha B, etal. Incremental Classification Rules Based on Association Rules Using Formal Concept Analysis//Proc of the 4th International Conference on Machine Learning and Data Mining in Pattern Recognition. Leipzig, Germany, 2005:11-20.

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  • 1王志华,尹项根,张小波,黄雄,杨经超.利用CVT捕捉电压行波实现故障测距的分析与实践[J].电力系统自动化,2004,28(22):63-68. 被引量:29
  • 2孙雅明,王俊丰.基于分形理论的输电线路故障类型识别新方法[J].电力系统自动化,2005,29(12):23-28. 被引量:25
  • 3王国胤,施鸿宝,邓伟.基于NARA模型和筛选方法的并行神经网络体系结构[J].计算机学报,1996,19(9):679-686. 被引量:2
  • 4电力行业继电保护标准化技术委员会.DLT 357-2010输电线路行波故障测距装置技术条件[S].北京:中国电力出版社,2010.
  • 5Zhang N,Kezunovic M.A real time fault analysis tool for monitoring operation of transmission line protective relay[J].Electric Power Systems Research,2007,77(3-4):361-370.
  • 6Upendara J,Guptaa C P,Singha G K.Fault classification scheme based on the adaptive resonance theory[J].Electric Power Components and Systems,2010(38):424-444.
  • 7Pradhan A K,Mohanty S R,Routray A.Neural fault classifier for transmission line protection-a modular approach[C]//Power Engineering Society General Meeting.Montreal,Quebec,Canada:IEEE PES,2006:1-4244-0493,1-5.
  • 8Pedrycz W,Waletzky J.Fuzzy clustering with partial supervision[J].IEEE Trans.on Systems,Man and Cybernetics,1997,27(5):787-795.
  • 9Stutz C,Thomas A R.Classification and prediction of road traffic using application-specific fuzzy clustering[J].IEEE Trans.on Fuzzy Systems,2002,10(3):297-308.
  • 10Bensaid A M,Hall L O,Bezdek J C,et al.Partially supervised clustering for image segmentation[J].Pattern Recognition,1996,29(5):859-871.

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