Outlier detection has attracted extensive attention in medical, financial, telecommunications and other fields. Although many related technologies have been proposed, most of them are faced with the problems of the neighborhood size of an object is difficult to determine and the distance in high-dimensional space is unreliable. To overcome these weaknesses, we propose a novel density-based outlier detection method that introduces the concept of Minimum the Sum of Edge Set and other related definitions in key attributes space. Based on the stability of Reverse Minimum the Sum of Edge Set, the proposed method can adaptively select the parameter representing the neighborhood size. In addition, some properties of the proposed local outlier factor are derived. Experiments on synthetic and real-world datasets demonstrate that our method is more effective than the existing outlier detection approaches.

Traffic characteristics over space and time constitute an important aspect of cellular networks in consideration of resource provision, traffic engineering and system optimization. Despite recent progress in revealing temporal dynamics and spatial inhomogeneity of cellular traffic, limited knowledge about traffic dependence is gained. One of challenges comes from the absence of sustained observations at a network-wide scale. In this paper, we make an analysis on the week-long traffic generated by a large population of users in a city of China, and model traffic dependence along both space and time dimensions. The evaluation results suggest connections between spatio-temporal dependence of cellular traffic and the organization of human lives. Region differences are observed to impact traffic dependence to a great extent. Additionally, interactive knowledge between space and time enhances traffic prediction with a decrease in root-mean-square error of 2.8%~25.2%. We believe that these achievements will benefit multiple research and development areas such as network deploying and simulation researches.