Outlier detection based on cluster outlier factor and mutual density

被引：0

作者：

Zhang Z. ^{[1
,2
]}

Qiu J. ^{[1
]}

Liu C. ^{[1
]}

Zhu M. ^{[1
]}

Zhang D. ^{[3
]}

机构：

[1] School of Information Science and Engineering, Yanshan University, Qinhuangdao

[2] The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Qinhuangdao

[3] Hebei Education Examinations Authority, Shijiazhuang

来源：

Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2019年 / 25卷 / 09期

关键词：

Cluster outlier factor; Data mining; Mutual density; Outlier; Γ; density;

D O I：

10.13196/j.cims.2019.09.018

中图分类号：

学科分类号：

摘要：

Most outlier detection algorithms based on clustering often need to input parameters artificially, which was difficult to select a suitable parameter for different datasets. To solve this problem, an outlier detection algorithm based on cluster outlier factor and mutual density was proposed by combining the natural neighbor search algorithm of NOF algorithm with DPC algorithm. The mutual density and γ density was used to construct decision graph, and the data points with gamma-density anomalously large in decision graph were treated as cluster centers. According to the Cluster Outlier Factor(COF), the boundary of outlier cluster was detected to find the parameter automatically. The experiments showed that the proposed method could achieve good performance in clustering and outlier detection. © 2019, Editorial Department of CIMS. All right reserved.

引用

页码：2314 / 2323

页数：9

共 23 条

[1]

Denning D.E., An intrusion-detection Model, IEEE Transactions on Software Engineering, 13, 2, pp. 222-232, (1987)

[2]

Singh K., Upadhyaya S., Outlier detection: applications and techniques, International Journal of Computer Science, 9, 3, pp. 307-323, (2012)

[3]

Wen J., Tung A.K.H., Han J., Mining top-n local outliers in large databases, Proceedings of the 7th ACM SIGKDD International Conference, pp. 293-298, (2001)

[4]

Lin J., Keogh E., Fu A., Et al., Approximations to magic: finding unusual medical time series, Proceedings of IEEE Symposium on Computer-Based Medical Systems, pp. 329-334, (2005)

[5]

Zhao J., Lu C.T., Kou Y., Detecting region outliers in meteorological data, Proceedings of the 11th ACM International Symposium on Advances in Geographic Information Systems, pp. 49-55, (2003)

[6]

Bhattacharya G., Ghosh K., Chowdhury A.S., Outlier detection using neighborhood rank difference, Pattern Recognition Letters, 60-61, pp. 24-31, (2015)

[7]

Xue A., Ju S., He W., Et al., Study on algorithms for local outlier detection, Chinese Journal of Computer, 30, 8, pp. 1455-1463, (2007)

[8]

Hawkins D.M., Identification of outliers, Biometrics, 37, 4, (1980)

[9]

Knorr E.M., Ng R.T., A unified notion of outliers: properties and computation, Proceedings of the International Conference on Knowledge Discovery and Data Mining, pp. 219-222, (1997)

[10]

Wang Y., Zhang J., Zhao X., Contextual outlier mining algorithm based on particle swarm optimization, Journal of Taiyuan University of Science and Technology, 36, 5, pp. 327-332, (2015)

← 1 2 3 →