With the increasing complexity of mobile networks, it has become more and more difficult to perform effective management of mobile networks, which has led to more data to be evaluated and optimized. This article focuses on the performance evaluation of Long Term Evolution(LTE) networks by using two unsupervised learning techniques. Besides, this paper aims to identify the pros and cons of these two clustering algorithms as well. To achieve the above goals, different dimensional datasets for learning a process based on two classic unsupervised clustering methodsare introduced to this work. A Self-organized map (SOM) neural network and k-means are a comparison algorithm and the sample data with three different degree correlation coefficients features with 63 LTE cells, which is from a major European city. The purpose behind using these two methods is to see how different dimensions of the datasets can be used for testing clustering effec tiveness and we propose a method based on the features extracted from key performance indicators (KPIs) and Euclidean distance is used as the evaluation standard for the distance between different clusters and samples within clusters. The comparing results show that k-means has a better cluster performance in low dimension data set, whereas the SOM’s performance unsatisfactory. On the  other hand, the SOM’s clustering performance is better than k-means in high dimension and big data set and it could visualize results. It was verified that there is a significant difference in the obtained results using different clustering algorithms.