Sage Journals: Discover world-class research

Abstract

Outlier detection can detect a small amount of data which containing valuable information from a large number of data, and it has become a hot topic in data mining. In this article, a new algorithm is proposed, which is fast local outlier detection algorithm using $K$ kernel space. It is proposed to solve problem of the detection efficiency is not high because of the unevenness of density distribution in the outlier detection algorithm based on density, and the running time of the algorithm is obviously increased after introducing the reverse $K$ nearest neighbors algorithm. By introducing $K$ kernel space this algorithm divides the objects in data set into near $K$ neighborhood points and far $K$ neighborhood points, and reduces the number of data points which computation of the reverse $K$ neighborhood, so as to reduce the running time of the algorithm. The accuracy of outlier detection is improved by introducing reachable distance and reachable density to reduce the statistical fluctuation of distance. Finally, the effectiveness of the proposed algorithm is shown by simulation data set and real data set.

Keywords

Data mining outlier detection K kernel space reachable density

Get full access to this article

View all access options for this article.

References

Salehi

Leckie

Bezdek

J.C.

et al., Fast memory efficient local outlier detection in data streams (extended abstract), IEEE International Conference on Data Engineering 28(12) (2017), 3246–3260.

Nakamura

Kamidoi

Wakabayashi

et al., A decision method of attribute importance for classification by outlier detection, International Conference on Data Engineering Workshops, 2006.

Jin

Tung

A.K.

and Han

, Mining top-n local outiers in large databases, Proceedings of The Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2001, pp. 293–298.

Liu

Yang

et al., Local outlier mining algorithm using subspace partitioning, Journal of Chinese Computer System 32(8) (2011), 1628–1632.

Tan

P.N.

Steinbach

and Kumar

, Introduction to Data Mining, (First Edition), Addison wesley longman publishing Co, 2005, pp. 133–134.

Tong

Zhu

and Wu

, NOSEP: Nonoverlapping sequence pattern mining with gap constraints, IEEE Transactions on Cybernetics 48(10) (2018), 2809–2822.

Hido

Tsuboi

Kashima

et al., Inlier based outlier detection via direct density ratio estimation, Eighth IEEE International Conference on Data Mining IEEE Computer Society, 2008, pp. 223–232.

Hawkins

D.M.

, Identification of outliers, Biometrics 37(4) (1980), 860–861.

Hido

Tsuboi

Kashima

et al., Inlier-based outlier detection via direct density ratio estimation, Eighth IEEE International Conference on Data Mining IEEE Computer Society, 2008, pp. 223–232.

10.

Otey

M.E.

Ghoting

and Parthasarathy

, Fast distributed outlier detection in mixed attribute data sets, Data Mining and Knowledge Discovery 12(2-3) (2006), 203–228.

11.

Zhang

Hamm

N.A.S.

Meratnia

et al., Statistics based outlier detection for wireless sensor networks, International Journal of Geographical Information Science 26(8) (2012), 1373–1392.

12.

Guha

Rastogi

Shim

et al., CURE: An efficient clustering algorithm for large databases, Information Systems 26(1) (1998), 35–58.

13.

Sheikholeslami

and Zhang

, FindOut: Finding outliers in very large datasets, Knowledge and Information Systems 4(4) (2002), 387–412.

14.

Liu

and Wang

, Outlier detection based on local minima density, Information Technology, Networking, Electronic and Automation Control Conference, IEEE, 2016, pp. 718–723.

15.

Angiulli

Basta

and Pizzuti

, Distance-based detection and prediction of outliers, IEEE Transactions on Knowledge and Data Engineering 18(2) (2005), 145–160.

16.

C.P.

and Qin

X.L.

, A density-based local outlier detecting algorithm, Journal of Computer Research and Development 47(12) (2006), 2110–2116.

17.

Breunig

M.M.

, LOF: Identifying density-based local outliers, ACM Sigmod Record 29(2) (2000), 93–104.

18.

Jin

Tung

A.K.H.

Han

et al., Ranking outliers using symmetric neighborhood relationship, Lecture Notes in Computer Science 3918 (2006), 577–593.

19.

Han

Kamber

Jian

et al.,Data Mining Concept and Techniques, China machine press, 2012, pp. 364–365.

20.

Tang

Chen

W.C.

et al., Enhancing effectiveness of outlier detections for low density patterns, in: Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, Pacific-asia conf, on knowledge discovery and data mining (PAKDD), Taipei, Taiwan, 2002, pp. 45–84.

21.

Tao

,Outlier detection method based on clustering and density, South China University of Technology, 2014.

22.

Zou

Zhang

Song

and Ni

, Fast outlier detection algorithm based on local density, Journal of Computer Applications 37(10) (2017), 2932–2937.

Fast local outlier detection algorithm using K kernel space

Abstract

Keywords

Get full access to this article

References