[1] |
ROUSSEEUW P J, LEROY A M. Robust Regression and Outlier Detection[M]. New York: John Wiley & Sons, 1987.
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[2] |
JIANG F, SUI Y F, CAO C G. A hybrid approach to outlier detection based on boundary region[J]. Pattern Recognition Letter, 2011,32(14):1860-1870.
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[3] |
BREUNIG M M, KRIEGEL H P, NG R T, et al. LOF: Identifying density-based local outliers[C]// Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data. 2000:93-104.〖HJ0.27mm〗
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[4] |
耿志强,姬威,韩永明,等. 基于维度最大熵数据流聚类的异常检测方法[J]. 控制与决策, 2016,31(2):343-348.
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[5] |
江峰,王凯郦,于旭,等. 基于粗糙熵的离群点检测方法及其在无监督入侵检测中的应用[J]. 控制与决策, 2020,35(5):1199-1204.
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[6] |
袁钟,冯山. 基于邻域值差异度量的离群点检测算法[J]. 计算机应用, 2018,38(7):1905-1909.
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[7] |
袁钟,张贤勇,冯山. 邻域粗糙集中基于序列的混合型属性离群点检测[J]. 小型微型计算机系统, 2018,39(6):1317-1322.
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[8] |
PAWLAK Z. Rough sets[J]. International Journal of Computer and Information Sciences, 1982,11(5):341-356.
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[9] |
HU Q H, YU D R, XIE Z X. Neighborhood classifiers[J]. Expert Systems with Applications, 2008,34(2):866-876.
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[10] |
HU Q H, LIU J F, YU D R. Mixed feature selection based on granulation and approximation[J]. Knowledge-Based Systems, 2008,21(4):294-304.
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[11] |
HU Q H, YU D R, LIU J F, et al. Neighborhood rough set based heterogeneous feature subset selection[J]. Information Sciences, 2008,178(18):3577-3594.
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[12] |
SHANNON C E. A mathematical theory of communication[J]. The Bell System Technical Journal, 1948,27(3):379-423.
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[13] |
HU Q H, YU D R. Neighborhood entropy[C]// Proceedings of the 2009 International Conference on Machine Learning and Cybernetics. 2009,3:1776-1782.
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[14] |
CHEN Y M, WU K S, CHEN X H, et al. An entropy-based uncertainty measurement approach in neighborhood systems[J]. Information Sciences, 2014,279:239-250.
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[15] |
ZENG K, SHE K, NIU X Z. Feature selection with neighborhood entropy-based cooperative game theory[J]. Computational Intelligence and Neuroscience, 2014,2014. DOI:10.1155/2014/479289.
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[16] |
WILSON D R, MARTINEZ T R. Improved heterogeneous distance functions[J]. Journal of Artificial Intelligence Research, 1997,6(1):1-34.
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[17] |
YUAN Z, ZHANG X Y, FENG S. Hybrid data-driven outlier detection based on neighborhood information entropy and its developmental measures[J]. Expert Systems with Applications, 2018,112:243-257.
|
[18] |
盛魁,卞显福,董辉,等. 基于邻域粗糙集组合度量的混合数据属性约简算法[J]. 计算机应用与软件, 2020,37(2):234-239.
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[19] |
RAMASWAMY S, RASTOGI R, SHIM K. Efficient algorithms for mining outliers from large data sets[C]// Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data. 2000:427-438.
|
[20] |
CHEN Y M, MIAO D Q, ZHANG H. Y. Neighborhood outlier detection[J]. Expert Systems with Application, 2010,37(12):8745-8749.
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[21] |
袁钟. 基于邻域粗糙集的混合型属性离群点检测方法研究[D]. 成都:四川师范大学, 2018.
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[22] |
HARKINS S, HE H X, WILLIAMS G J, et al. Outlier detection using replicator neural networks[C]// Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery. 2002:170-180.
|
[23] |
江峰,杜军威,葛艳,等. 基于粗糙集理论的序列离群点检测[J]. 电子学报, 2011,39(2):345-350.
|
[24] |
张玉婷. 基于邻域粗糙度量的离群点检测方法研究[D]. 成都:四川师范大学, 2021.
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[25] |
谭阳. 基于粗糙熵的渐进式离群点检测方法研究[D]. 成都:四川师范大学, 2021.
|
[26] |
杨晓玲,张贤勇. 基于邻域粗糙隶属函数的离群点检测[J]. 计算机工程与设计, 2019,40(2):533-539.
|
[27] |
郭春. 基于数据挖掘的网络入侵检测关键技术研究[D]. 北京:北京邮电大学, 2014.
|
[28] |
TAN P N, STEINBACH M, KUMAR V. Introduction to Data Mining[M]. Pearson Education India, 2016.[1] ROUSSEEUW P J, LEROY A M. Robust Regression and Outlier Detection[M]. New York: John Wiley & Sons, 1987.
|
[2] |
JIANG F, SUI Y F, CAO C G. A hybrid approach to outlier detection based on boundary region[J]. Pattern Recognition Letter, 2011,32(14):1860-1870.
|
[3] |
BREUNIG M M, KRIEGEL H P, NG R T, et al. LOF: Identifying density-based local outliers[C]// Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data. 2000:93-104.〖HJ0.27mm〗
|
[4] |
耿志强,姬威,韩永明,等. 基于维度最大熵数据流聚类的异常检测方法[J]. 控制与决策, 2016,31(2):343-348.
|
[5] |
江峰,王凯郦,于旭,等. 基于粗糙熵的离群点检测方法及其在无监督入侵检测中的应用[J]. 控制与决策, 2020,35(5):1199-1204.
|
[6] |
袁钟,冯山. 基于邻域值差异度量的离群点检测算法[J]. 计算机应用, 2018,38(7):1905-1909.
|
[7] |
袁钟,张贤勇,冯山. 邻域粗糙集中基于序列的混合型属性离群点检测[J]. 小型微型计算机系统, 2018,39(6):1317-1322.
|
[8] |
PAWLAK Z. Rough sets[J]. International Journal of Computer and Information Sciences, 1982,11(5):341-356.
|
[9] |
HU Q H, YU D R, XIE Z X. Neighborhood classifiers[J]. Expert Systems with Applications, 2008,34(2):866-876.
|
[10] |
HU Q H, LIU J F, YU D R. Mixed feature selection based on granulation and approximation[J]. Knowledge-Based Systems, 2008,21(4):294-304.
|
[11] |
HU Q H, YU D R, LIU J F, et al. Neighborhood rough set based heterogeneous feature subset selection[J]. Information Sciences, 2008,178(18):3577-3594.
|
[12] |
SHANNON C E. A mathematical theory of communication[J]. The Bell System Technical Journal, 1948,27(3):379-423.
|
[13] |
HU Q H, YU D R. Neighborhood entropy[C]// Proceedings of the 2009 International Conference on Machine Learning and Cybernetics. 2009,3:1776-1782.
|
[14] |
CHEN Y M, WU K S, CHEN X H, et al. An entropy-based uncertainty measurement approach in neighborhood systems[J]. Information Sciences, 2014,279:239-250.
|
[15] |
ZENG K, SHE K, NIU X Z. Feature selection with neighborhood entropy-based cooperative game theory[J]. Computational Intelligence and Neuroscience, 2014,2014. DOI:10.1155/2014/479289.
|
[16] |
WILSON D R, MARTINEZ T R. Improved heterogeneous distance functions[J]. Journal of Artificial Intelligence Research, 1997,6(1):1-34.
|
[17] |
YUAN Z, ZHANG X Y, FENG S. Hybrid data-driven outlier detection based on neighborhood information entropy and its developmental measures[J]. Expert Systems with Applications, 2018,112:243-257.
|
[18] |
盛魁,卞显福,董辉,等. 基于邻域粗糙集组合度量的混合数据属性约简算法[J]. 计算机应用与软件, 2020,37(2):234-239.
|
[19] |
RAMASWAMY S, RASTOGI R, SHIM K. Efficient algorithms for mining outliers from large data sets[C]// Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data. 2000:427-438.
|
[20] |
CHEN Y M, MIAO D Q, ZHANG H. Y. Neighborhood outlier detection[J]. Expert Systems with Application, 2010,37(12):8745-8749.
|
[21] |
袁钟. 基于邻域粗糙集的混合型属性离群点检测方法研究[D]. 成都:四川师范大学, 2018.
|
[22] |
HARKINS S, HE H X, WILLIAMS G J, et al. Outlier detection using replicator neural networks[C]// Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery. 2002:170-180.
|
[23] |
江峰,杜军威,葛艳,等. 基于粗糙集理论的序列离群点检测[J]. 电子学报, 2011,39(2):345-350.
|
[24] |
张玉婷. 基于邻域粗糙度量的离群点检测方法研究[D]. 成都:四川师范大学, 2021.
|
[25] |
谭阳. 基于粗糙熵的渐进式离群点检测方法研究[D]. 成都:四川师范大学, 2021.
|
[26] |
杨晓玲,张贤勇. 基于邻域粗糙隶属函数的离群点检测[J]. 计算机工程与设计, 2019,40(2):533-539.
|
[27] |
郭春. 基于数据挖掘的网络入侵检测关键技术研究[D]. 北京:北京邮电大学, 2014.
|
[28] |
TAN P N, STEINBACH M, KUMAR V. Introduction to Data Mining[M]. Pearson Education India, 2016.
|