Efficient outlier detection method based on Voronoi diagram

Computer Engineering and Applications ›› 2008, Vol. 44 ›› Issue (3): 178-179.

• 数据库与信息处理 • Previous Articles Next Articles

Efficient outlier detection method based on Voronoi diagram

QU Ji-lin

Department of Computer and Information Engineering，Shandong University of Finance，Ji’nan 250014，China

Received:1900-01-01 Revised:1900-01-01 Online:2008-01-21 Published:2008-01-21
Contact: QU Ji-lin

一种基于Voronoi图的高效异常检测方法

曲吉林

山东财政学院计算机与信息工程学院，济南 250014

通讯作者: 曲吉林

Abstract

Abstract: Outlier detection is an integral part of data mining.In this paper，we propose a new outlier detection method based on Voronoi diagram.The new method measures the outlier factor automatically by Voronoi neighborhoods without parameter，which provides highly-accurate outlier detection and reduces the time complexity from O（n²） to O（nlogn）.

Key words: data mining, outlier detection, Voronoi diagram

摘要： 提出了一种新的基于Voronoi图的异常检测方法。采用Voronoi图来确定对象间的邻近关系，定义了一种新的异常因子，算法的时间复杂性为O（nlogn）。实验结果表明，同现有的算法相比具有较高的检测效率和准确性。

关键词: 数据挖掘, 异常检测, Voronoi图

QU Ji-lin. Efficient outlier detection method based on Voronoi diagram[J]. Computer Engineering and Applications, 2008, 44(3): 178-179.

曲吉林. 一种基于Voronoi图的高效异常检测方法[J]. 计算机工程与应用, 2008, 44(3): 178-179.

[1]	ZONG Xiaoping, TAO Zeze. Knowledge Tracing Model Based on Mastery Speed [J]. Computer Engineering and Applications, 2021, 57(6): 117-123.
[2]	GAO Tianyu, WANG Qingrong, YANG Lei. Data Mining Model Based on Attribute Dependability Enhancement of Rough Set [J]. Computer Engineering and Applications, 2021, 57(3): 87-93.
[3]	MA Yang, ZHAO Xujun. Multi-source Outlier Detection Algorithm Based on Relevant Subspace [J]. Computer Engineering and Applications, 2021, 57(17): 88-95.
[4]	ZHANG Nianpeng, WU Xu, ZHU Qiang. Entropy-Based Oversampling Framework [J]. Computer Engineering and Applications, 2021, 57(13): 96-101.
[5]	ZHOU Yu, ZHU Wenhao, FANG Qian, BAI Lei. Survey of Outlier Detection Methods Based on Clustering [J]. Computer Engineering and Applications, 2021, 57(12): 37-45.
[6]	ZHANG Bowen, LIU Zhi, SANG Guoming. Anomaly Detection Algorithm Based on Kernel Density Fluctuation [J]. Computer Engineering and Applications, 2021, 57(12): 132-136.
[7]	RAO Jiawang, MA Ronghua. Improved Kernel Density Estimator Based Spatial Point Density Algorithm [J]. Computer Engineering and Applications, 2021, 57(11): 260-265.
[8]	WANG Jie, CHEN Zhigang, LIU Jialing, CHENG Hongbing. Privacy Behavior Mining Technology for Cloud Computing Based on Clustering [J]. Computer Engineering and Applications, 2020, 56(5): 80-84.
[9]	HE Huanye, LIN Guoyuan, GU Hao, FANG Menghua. Improved LOF Algorithm in Cloud Virtual Machine Anomaly Detection Scenario [J]. Computer Engineering and Applications, 2020, 56(23): 80-86.
[10]	WANG Zilong, LI Jin, SONG Yafei. Improved K-means Algorithm Based on Distance and Weight [J]. Computer Engineering and Applications, 2020, 56(23): 87-94.
[11]	JI Wenlu, WANG Hailong, SU Guibin, LIU Lin. Review of Recommendation Methods Based on Association Rules Algorithm [J]. Computer Engineering and Applications, 2020, 56(22): 33-41.
[12]	YI Junyan, WU Boya, YONG Qiaoling. Research on Clustering Algorithm of Elastic Net with Weighted Characteristics [J]. Computer Engineering and Applications, 2020, 56(22): 55-65.
[13]	QIN Fengting, YANG Youlong, QIU Haiquan. Sparse Subspace-Based Method for Local Outlier Detection [J]. Computer Engineering and Applications, 2020, 56(19): 152-159.
[14]	LIU Wenfen, MU Xiaodong, HUANG Yuehua. Anomaly Detection Method Based on Multi-resolution Grid [J]. Computer Engineering and Applications, 2020, 56(17): 78-85.
[15]	ZHAO Xiaoyong, WANG Ningning, WANG Lei. Research of Outlier Ensemble Mining Based on Active Learning [J]. Computer Engineering and Applications, 2020, 56(12): 112-117.

Efficient outlier detection method based on Voronoi diagram

一种基于Voronoi图的高效异常检测方法

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics