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The explosive growth of data vol-ume in mobile networks makes fast online di-agnose a pressing search problem. In this paper, an object-oriented detection framework with a two-step clustering, named as Hourglass Clus-tering, is given. Where three object parameters are chosen as Synthetical Quality of Experience (SQoE) Key Quality Indicators (KQIs) to re-flect accessibility, integrality, and maintainabil-ity of networks. Then, we choose represented Key Performance Indicators (rKPIs) as cause parameters with correlation analysis. For these two kinds of parameters, a hybrid algorithm combining the self-organizing map (SOM) and k-medoids is used for clustering them into dif-ferent types. We apply this framework to online anomaly detection in Cellular Networks, named SQoE-driven Anomaly Detection and Cause Location System (SQoE-ADCL). Our experi-ments with real 4G data show that besides fast online detection, SQoE-ADCL makes a better soft decision instead of a traditional hard deci-sion. Furthermore, it is also a general way of being applied to other similar applications in big data.