Deep Clustering of Mobile Network Data with Sparse Autoencoders

Unsupervised machine learning methods, such as clustering algorithms could be powerful tools for automation. By simplifying data through structuring, these algorithms can help network management use-cases where autonomous agency or elevated levels of cognition is required. Recent developments in deep learning allow clustering algorithms to gain unprecedented insight into the data, creating meaningful clusters as a result. In this paper, we propose a Sparse Clustering Autoencoder, capable of autonomously encoding cell behavior into a graph-like representation we call Network State Transition Graphs. We compare our proposed algorithm against other deep learning-based clustering algorithms, and demonstrate its utility on data from a real, large-scale mobile network deployment.