Collected telecom data traffic has boomed in recent years, due to the development of 4G mobile devices and other similar highspeed\nmachines.Theability to quickly identify unexpected traffic data in this streamis critical formobile carriers, as it can be caused\nby either fraudulent intrusion or technical problems. Clustering models can help to identify issues by showing patterns in network\ndata, which can quickly catch anomalies and highlight previously unseen outliers. In this article, we develop and compare clustering\nmodels for telecom data, focusing on those that include time-stamp information management. Two main models are introduced,\nsolved in detail, and analyzed: Gaussian Probabilistic Latent Semantic Analysis (GPLSA) and time-dependent Gaussian Mixture\nModels (time-GMM). These models are then compared with other different clustering models, such as Gaussian model and GMM\n(which do not contain time-stamp information). We perform computation on both sample and telecom traffic data to show that\nthe efficiency and robustness of GPLSA make it the superior method to detect outliers and provide results automatically with low\ntuning parameters or expertise requirement.
Loading....