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 ...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.展开更多
Outlier detection is a very important type of data mining,which is extensively used in application areas.The traditional cell-based outlier detection algorithm not only takes a large amount of time in processing massi...Outlier detection is a very important type of data mining,which is extensively used in application areas.The traditional cell-based outlier detection algorithm not only takes a large amount of time in processing massive data,but also uses lots of machine resources,which results in the imbalance of the machine load.This paper presents an algorithm of the MapReduce-based and cell-based outlier detection,combined with the single-layer perceptron,which achieves the parallelization of outlier detection.These experiments show that this improved algorithm is able to effectively improve the efficiency of the outlier detection as well as the accuracy.展开更多
基金supported by the National Basic Research Program of China (973 Program: 2013CB329004)
文摘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.
基金Supported by the National High Technology Research and Development Program of China(863 Program)(2012AA040910)
文摘Outlier detection is a very important type of data mining,which is extensively used in application areas.The traditional cell-based outlier detection algorithm not only takes a large amount of time in processing massive data,but also uses lots of machine resources,which results in the imbalance of the machine load.This paper presents an algorithm of the MapReduce-based and cell-based outlier detection,combined with the single-layer perceptron,which achieves the parallelization of outlier detection.These experiments show that this improved algorithm is able to effectively improve the efficiency of the outlier detection as well as the accuracy.
