Outlier detection based on neighborhood chain

被引：0

作者：

Liang S.-Y. ^{[1
,2
]}

Han D.-Q. ^{[1
,2
]}

机构：

[1] College of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an

[2] CETC Key Laboratory of Aerospace Information Applications, China Electronics Technology Group Corporation, Shijiazhuang

来源：

Kongzhi yu Juece/Control and Decision | 2019年 / 34卷 / 07期

关键词：

Data mining; Euclidean distance; Local density; Local outlier factor; Neighborhood chain; Outlier detection;

D O I：

10.13195/j.kzyjc.2017.1649

中图分类号：

学科分类号：

摘要：

Many research works in the area of outlier detection are focused on the so called "density-based" methods. Such kind of methods can counter-act many drawbacks of the traditional outlier detection methods. However, most existing density-based methods use geometric-distance-based approaches to estimate the data point's local density, which leads to incorrect results in certain cases. To resolve the problem, the traditional local density estimation method is substituted by a neighborhood-chain-based method, and a new outlier detection method is proposed. Compared to the local outlier factor (LOF) and several of related modifications, the proposed one can find the outliers more accurately. © 2019, Editorial Office of Control and Decision. All right reserved.

引用

页码：1433 / 1440

页数：7

共 23 条

[1]

Bolton R.J., Hand D.J., Statistical fraud detection: A review (with discussion), Statistical Science, 17, 3, (2002)

[2]

Tang B., He H.B., A local density based approach for outlier detection, Neurocomputing, 241, 2, pp. 171-180, (2017)

[3]

Jin W., Tung A.K., Han J., Mining top-n local outliers in large databases, Proc of the 7th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining, pp. 293-298, (2001)

[4]

Barnett V., Lewis T., Outliers in Statistical Data, pp. 335-338, (1994)

[5]

Hwkins D.M., Identification of Outliers, pp. 613-615, (1980)

[6]

Zhang T., Ramakrishnan R., Livny M., BIRCH: A new data clustering algrithm and its applications, Data Mining and Knowledge Discovery, 1, 2, pp. 141-182, (1997)

[7]

Brito M., Chavez E., Quiroz A., Connectivity of the mutual k-nearest-neighbor graph in clustering and outlier detection, Statistics & Probability Letters, 35, 1, pp. 33-42, (1997)

[8]

Knorr E.M., Ng R.T., A unified notion of outliers: Properties and computation, Proc of the 3rd ACM Int Conf on Knowledge Discovery and Data Mining, pp. 219-222, (1997)

[9]

Knorr E.M., Ng R.T., Algorithms for mining distance based outliers in large datasets, Proc of the 24th Int Conf on Very Large Data Bases, pp. 392-403, (1998)

[10]

Breunig M.M., Kriegel H.P., Ng R.T., Et al., LOF: Identifying density-based local outliers, Proc of ACM Sigmod Record, pp. 93-104, (2000)

← 1 2 3 →