Alarm log plays a crucial role in the network area as a valuable source of information for anomaly detection and reporting network failures. Manual analysis of such logs is time-consuming and costly because they involve an extensive amount of data. On the other hand, the automatic detection of useful information can be also quite challenging. As a result, finding suitable methods to process these logs in a proper way is a well-established problem in the network analysis area. In this research, we propose unsupervised machine learning techniques to mine data logs and thus provide meaningful information about possible causes and cascade effect in a network failure.