Anomalous Cell Detection with Kernel Density-Based Local Outlier Factor

作     者：Miao Dandan Qin Xiaowei Wang Weidong 

作者机构：Key Laboratory of Wireless-Optical Communications Chinese Academy of Sciences School of Information Science and TechnologyUniversity of Science and Technology of China 

出 版 物：《China Communications》 (中国通信（英文版）)

年 卷 期：2015年第12卷第9期

页      面：64-75页

核心收录：

学科分类：0710[理学-生物学] 12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 071009[理学-细胞生物学] 09[农学] 0901[农学-作物学] 090102[农学-作物遗传育种] 

基　　金：supported by the National Basic Research Program of China (973 Program: 2013CB329004) 

主　　题：data mining key performance indicators kernel density based local outlier factor density perturbation anomalous cell detection 

摘      要：Since data services are penetrating into our daily life rapidly, the mobile network becomes more complicated, and the amount of data transmission is more and more increasing. In this case, the traditional statistical methods for anomalous cell detection cannot adapt to the evolution of networks, and data mining becomes the mainstream. In this paper, we propose a novel kernel density-based local outlier factor(KLOF) to assign a degree of being an outlier to each object. Firstly, the notion of KLOF is introduced, which captures exactly the relative degree of isolation. Then, by analyzing its properties, including the tightness of upper and lower bounds, sensitivity of density perturbation, we find that KLOF is much greater than 1 for outliers. Lastly, KLOFis applied on a real-world dataset to detect anomalous cells with abnormal key performance indicators(KPIs) to verify its reliability. The experiment shows that KLOF can find outliers efficiently. It can be a guideline for the operators to perform faster and more efficient trouble shooting.