A method and system for detecting abnormal values in an LTE network is provided: dividing measured data into a training and a testing set; defining clusters and parameters in the training set, and finding the cluster to which each point belongs using clustering algorithms; calculating a likelihood of each point based on parameters and clustering results; assigning the likelihood into an abnormal, an intermediate or a normal region according to a set warning and alarming threshold; and applying a calculated model to the testing set, the likelihood of each point is calculated and assigned to a region, thereby finding abnormal values in the testing set. The variation of data points versus time may be better understood by introducing time axes into the model, thereby multiple abnormal values may be discovered from a sequence of multiple points. The method can immediately detect abnormal values and the error rate is low.