An improved outlier detection algorithm K-LOF based on density
DOI: 10.23977/cpcs.2017.21001 | Downloads: 68 | Views: 3966
Wang Baoyi 1, Luo Xiangyu 1, Zhang Shaomin 1
1 School of Control and Computer Engineering, North China Electric Power University, Baoding, 071003, China
Corresponding AuthorZhang Shaomin
The local outlier factor (LOF) algorithm is one of the representative algorithms based on the density outlier detection algorithm. But the algorithm has the problem of high time complexity, not suitable for large data sets and high dimensional data set. Therefore, this paper proposes a new outlier detection algorithm, clustering the data sets determines the data center of data space through the K-means clustering algorithm, building data set primary model by setting the distance threshold of the data set object to the data center, and optimizing the screening process combined the neighbor distribution of data objects. Although the use of clustering algorithm for abnormal data set screening will increase the computational complexity of the algorithm, but the data center space once identified will no longer need to repeat the calculation, so with the increase of data, the advantages of the algorithm will become more and more obvious. After testing, the algorithm can effectively improve the detection accuracy of anomaly factors, and reduce the computational complexity of the algorithm, and can complete the local outlier detection.
KEYWORDSData mining; Clustering algorithm; Outlier detection
CITE THIS PAPER
Shaomin, Z., Xiangyu, L. Baoyi, W. (2017) An improved outlier delection algorithm K-LOF based on density. Computing, Performance and Communication Systems (2017) Vol.2, Num.1: 1-7.
 ZUO Jin, CHEN Zemao. Anomaly Detection Algorithm Based on Improved K-means Clustering[J]. Computer Science, 2016, 43(8):258-261.
 CAO Ke-Yan, LUAN Fang-Jun,SUN Huan-Liang,DING Guo-Hui. Density-based Local Outlier Detection on Uncertain Data[J]. Chinese Journal of Computer.2016,(39):1-15.
 Fu PG, Hu XH. SLDOF: Anomaly Detection Algorithm Based on the Local Distance of Density-based Sampling Data[J]. Ruan Jian Xue Bao/Journal of Software (in Chinese). 2016 :1-16.
 ZHANG Zhong-ping, LIANG Yong-xin. Stream Data Outlier Mining Algorithm Based on Reverse k Nearest Neighbors[J].Computer Engineering. 2009, 35(12):11-13.
 GU Ping, LIU Hai-bo,LUO Zhi-heng. Multi-clustering based outlier detect algorithm[J]. Application Research of Computers.2013, 30(3):751-753.
 Angiulli F, Fassetti F. DOLPHIN:An efficient algorithm for mining distance-based outliers in very large datasets[J]. Acm Transactions on Knowledge Discovery from Data, 2009, 3(1):1-57.
 WANG Qian, LIU Shu-zhi. Improvement of local outliers mining based on density[J].Application Research of Computers. 2014, 31(6):1693-1696.
 HU Caiping and QIN Xiaolin.A Density-Based Local Outlier Detecting Algorithm[J].Journal of Computer Research and Development.2010, 47(12):2110-2116.
 HU Liang, REN Wei-wu,REN Fei,LIU Xiao-bo,JIN Gang. Anomaly Detection Algorithm Based on Improved Density Clustering[J].Journal of Jilin University(Science Edition).2009, 47(5):954-960.
 HU Wei, LI Yong, CAO Yijia, ZHANG Zhipeng, ZHAO Qingzhou, DUAN Yilong.Fault identification based on LOF and SVM for smart distribution network.Electric Power Automation Equipment[J].2016, 36(6):7-12.
 Tan P N, Steinbach M, Kumar V. Introduction to data mining[M]. FAN Ming, FAN Hongjian, translated.Beijing,China: The People’s Posts and Telecommunications Press[J]. 2010: 328-330.
 YAN Yingjie,SHENG Gehao,LIU Yadong,DU Xiuming,WANG Hui,JIANG Xiuchen. Anomalous State Detection of PowerTransformer Based on Algorithm Sliding Windows and Clustering. High Voltage Engineering.2016, 42(12):4020-4025.