Anomalous cell detection with kernel density-based local outlier factor
Anomalous cell detection with kernel density-based local outlier factor
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DOI：
10.1109/cc.2015.7275260
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发表时间：
2015-09
影响因子：
4.1
通讯作者：
Dandan Miao;Xiaowei Qin;Weidong Wang
中科院分区：
文献类型：
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作者：
Dandan Miao;Xiaowei Qin;Weidong Wang
随着数据业务的快速渗透，移动的网络变得越来越复杂，数据传输量也越来越大。在这种情况下，传统的统计方法检测异常小区已经不能适应网络的演进，数据挖掘成为主流。在本文中，我们提出了一种新的基于核密度的局部离群因子（KLOF）分配的程度是一个离群到每个对象。首先，引入了KLOF的概念，它准确地捕获了相对隔离度。然后，通过分析其性质，包括上下界的紧密性，密度扰动的敏感性，我们发现KLOF远大于1的离群值。最后，将KLOF应用于真实数据集，检测具有异常关键性能指标（KPI）的异常细胞，以验证其可靠性。实验表明，KLOF算法能有效地发现异常点。它可以作为操作员更快、更有效地进行故障排除的指导方针。
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, KLOF is 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.