摘要: Statistical methods have been used to handle noisy and incomplete data. In this note, I shall argue that AI, especially AI modelling techniques, has an important role play in helping ensure the data quality. The management of outliers will be illustrate point. particular, two different ways using domain knowledge help distinguish between outlying noise free are described, techniques found particularly useful for type knowledge-based outlier analysis.
springer.com
sci-hub.st 参考文章(35)
Jan Zytkow, Willi Klösgen, Machine discovery terminology knowledge discovery and data mining. pp. 463- 473 ,(1994)
Ronald J. Brachman, Tej Anand, The process of knowledge discovery in databases knowledge discovery and data mining. pp. 37- 57 ,(1996)
Gongxian Cheng, Xiaohui Liu, John Xingwang Wu, Noise and uncertainty management in intelligent data modeling national conference on artificial intelligence. pp. 263- 268 ,(1994)
V. Barnett, The Ordering of Multivariate Data Journal of the Royal Statistical Society: Series A (General). ,vol. 139, pp. 318- 344 ,(1976) , 10.2307/2344839
Michael J. Pazzani, Pat Langley, Douglas H. Fisher, Concept formation knowledge and experience in unsupervised learning Morgan Kaufmann Publishers Inc.. ,(1991)
Vladimir Vapnik, Isabelle Guyon, Nada Matic, Discovering informative patterns and data cleaning knowledge discovery and data mining. pp. 181- 203 ,(1996)
David E. Rumelhart, James L. McClelland, , Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations Computational Models of Cognition and Perception. ,(1986) , 10.7551/MITPRESS/5236.001.0001
Teuvo Kohonen, Self-Organizing Maps ,(1995)
John X Wu, Gongxian Cheng, Xiaohui Liu, Reasoning about Outliers by Modelling Noisy Data intelligent data analysis. pp. 549- 558 ,(1997) , 10.1007/BFB0052870
H. Ritter, K. Schulten, On the stationary state of Kohonen's self-organizing sensory mapping Biological Cybernetics. ,vol. 54, pp. 99- 106 ,(1986) , 10.1007/BF00320480