摘要
The explosive growth of data volume in mobile networks makes fast online diagnose a pressing search problem. In this paper, an object-oriented detection framework with a two-step clustering, named as Hourglass Clustering, is given. Where three object parameters are chosen as Synthetical Quality of Experience（SQo E） Key Quality Indicators（KQIs） to reflect accessibility, integrality, and maintainability of networks. Then, we choose represented Key Performance Indicators（r KPIs） as cause parameters with correlation analysis. For these two kinds of parameters, a hybrid algorithm combining the self-organizing map（SOM） and
The explosive growth of data volume in mobile networks makes fast online diagnose a pressing search problem. In this paper, an object-oriented detection framework with a two-step clustering, named as Hourglass Clustering, is given. Where three object parameters are chosen as Synthetical Quality of Experience(SQo E) Key Quality Indicators(KQIs) to reflect accessibility, integrality, and maintainability of networks. Then, we choose represented Key Performance Indicators(r KPIs) as cause parameters with correlation analysis. For these two kinds of parameters, a hybrid algorithm combining the self-organizing map(SOM)
基金
supported by the National Basic Research Program of China(973 Program:2013CB329004)
the Fundamental Research Funds for the Central Universities
