[1]苗永春,程艳.离群点检测方法及其在大数据时代下的改进方法研究[J].江西师范大学学报(自然科学版),2014,(05):454-458.
　MIAO Yong-chun,CHENG Yan.The Outlier Detection Method and Its Improvement in the Eea of Big Data[J].Journal of Jiangxi Normal University:Natural Science Edition,2014,(05):454-458.

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离群点检测方法及其在大数据时代下的改进方法研究()

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参考文献/References:

[1] Barwick H.The “four Vs ” of big data.Implementing information infrastructure symposium [EB/OL].
[2012-10-02].http://www.computerworld.com.au/article/396198/iiis_four_vs_big_data/.
[2] Han Jiawei,Kamber.Data mining:concepts and techniques [M].2ed.San Francisco:Morgan Kaufmann,2006.
[3] 薛安荣,姚林,鞠时光,等.离群点挖掘方法综述 [J].计算机科学,2008,35(11):13-27.
[4] 薛安荣,鞠时光,何伟华,等.局部离群点挖掘算法研究 [J].计算机学报,2007,30(8):1456-1463.
[5] 黄洪宇,林甲祥,陈崇成,等.离群数据挖掘综述 [J].计算机应用研究,2006,8:8-11.
[6] Hawkins D.Identification of outliers [M].London:Chapman and Hall,1980.
[7] 徐翔,刘建伟,罗雄麟.离群点挖掘研究 [J].计算机应用研究,2009,26(1):34-39.
[8] Barnett V,Lewis T.Outliers in statistical data [M].New York:John Wiley & Sons,1994.
[9] 金义富,邓明.基于统计的离群数据挖掘与分析 [J].湛江师范学院学报,2007,28(6):71-73.
[10] 李志云.数据挖掘中离群点检测的非参数方法研究 [J].微型电脑应用,2013,29(8):46-47.
[11] Paul S T,Fung K Y.A Generalized extreme studentized residual multiple-outlier-detection procedure in linear regression [J].Techno-metrics,1991,33:339-348.
[12] 史东辉,张春阳,蔡庆生.离群数据的挖掘方法研究 [J].小型微型计算机系统,2001,22(10):234-236.
[13] 杨茂林.离群检测算法研究 [D].武汉:华中科技大学,2012.
[14] Knorr E M,Ng R T.Algorithms for mining distance-based outliers in large datasets [C]//New York:Proc of Int Conf Very Large Data-bases(VLDB'98),1998:392-403.
[15] Knorr E,Ng R.Findingintensional knowledge of distance-based outliers [C]//Scotland:Proc of the 25 th VLDB Conference Edin-burgh,1999:211-222.
[16] Angiulli F,Pizzuti C.Fast outlier detection in high dimensional spaces [EB/OL].
[2012-10-16].http://www.researchgate.net/publication/220699183_Fast_Outlier_Detection_in_High_Dimensional_Spaces.
[17] Bay S D,Schwabacher M.Mining distance-based outliers in near linear time with randomization and a simple pruning rule [C].Washington,DC:Sigkdd,2003.
[18] An Jiawei,Kamber M.Datamining:concepts and techniques [M].New York:Academic Press,2001.
[19] 胡彩平,秦小麟.一种基于密度的局部离群点检测算法DLOF [J].计算机研究与发展,2010,47(12):2110-2116.
[20] 杨福萍,王洪国,等.基于聚类划分的两阶段离群点检测算法 [J].计算机应用研究,2013,30(7):1943-1945.
[21] Spiros Papadimitriou,Hiroyuki Kitagawa,et al.LOCI:fast outlier detection using the local correlation integral [EB/OL].
[2013-10-12] 10.1109/ICDE.2003.1260802.
[22] Breuning M M,Kriegel H P,Ng R T,et al.LOF:identifying density-based local outliers [C].Dallas:ACM Press,2000:93-104.
[23] 施化吉,周书勇,李星毅,等.基于平均密度的孤立点检测研究 [J].电子科技大学学报,2007,36(6):1286-1288.
[24] Aggarwal C C,Yu P.Findinggeneralized projected clusters in high dimensional spaces [C].Dallas:ACM Press,2000:70-81.
[25] 张卫旭,尉宇.基于密度的局部离群点检测算法 [J].计算机与数字工程,2010,38(10):11-14.
[26] Ng R,Han J.Efficient and effective clustering methods for spatial data mining [C].California:Morgan Kaufmann Publishers Inc,1994,144-155.
[27] 蒋盛益,李霞,郑琪.数据挖掘原理与实践 [M].北京:电子工业出版社,2011.
[28] Xu R,Wunsch II D.Survey of clustering algorithms [J].IEEE Transactions on Neural Networks,2005,16(3):645-678.
[29] Das K,Schneider J.Detecting anomalous records in categorical dataset [C].New York:ACM,2007,220-229.
[30] Markou M,Singh S.Novelty detection:a review-part2:neural network based approaches [J].Signal Processing,2003,83(12):2499-2521.
[31] Wong W K,Moore A,Cooper G,et al.Bayesian network anomaly pattern detection for disease outbreaks [C].Washington DC:AAAI Press,2003:808-815.
[32] Ratsch Q Mika S,Scholkopf B.Constructing boosting algorithms from svms:An application to one-class classification [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(9):1184-1199.
[33] Mahoney M V,Chan P K.Learning rules for anomaly detection of hostile network traffic [C].Washington DC:IEEE,2003:601-604.
[34] 崔贯勋,朱庆生.一种改进的基于密度的离群数据挖掘算法 [J].计算机应用,2007,27(3):560-573.
[35] 古平,刘海波,罗志恒.一种基于多重聚类的离群点检测算法 [J].计算机应用研究,2013,30(3):751-754.
[36] 赵战营,成长生.基于聚类分析局部离群点挖掘改进算法的研究与实现 [J].计算机应用与软件,2010,27(11):255-258.
[37] Agrawal R,Gehrke J,Gunopulos D,et al.Automatic subspace clustering of high dimensional data for data mining applications [EB/OL].
[2013-10-17].http://wenku.baidu.com/link?url=GuhDQJR7Xnz0D_PifjZVa1jMJtC- iFqlbh_qphD8egqzM_2fkYZJLCaj8sfpFuJ 5gocOgVM3vv- U2c_NX_AlhbEd0BhLCW4bagPjP3CYF 1Qmq.
[38] 吴晓燕.高维数据空间中离群点检测算法的研究 [D].南京:南京财经大学,2010.
[39] 王芳.基于属性重要度的属性约简算法研究 [D].成都:电子科技大学,2011.
[40] Ye Zhengwang.The research of intrusion detection algorithms based on the clustering of information entropy [C].Wuhan:Hubei University of Technology,2010:552-555.
[41] 陈源,曾德胜,谢冲.基于聚类的属性约简方法 [J].计算机系统应用,2009,(5):173-176.
[42] 孟小峰,慈祥.大数据管理、概念技术与挑战 [J].计算机研究与发展,2013,50(1):146-169.