An Infinite Multivariate Categorical Mixture Model for Self-Diagnosis of Telecommunication Networks

The diagnosis of telecommunication networks remains a challenging task, mainly due to the large variety and volume of data from which the root causes have to be inferred. Expert systems, supervised machine learning, or Bayesian networks require expensive and time consuming data labeling or processing by experts. In this paper, we propose an Infinite Multivariate Categorical Mixture Model for clustering patterns of faults from data gathered from telecommunication networks. The model is able to automatically identify the number of clusters necessary to explain the data using the Dirichlet process prior. We show how to use Variational Inference to derive an Expectation-Maximization (EM) like algorithm to perform inference on the model. We apply our model on synthetic data generated from an expert Bayesian network of a Fiber- To- The-Home (FTTH) Gigabit capable Passive Optical Network (GPON). We show that the model discovers the patterns linked to the root causes of the faults with up to 96 % accuracy in an unsupervised manner. We also apply our method on real data gathered from the FTTH network and the local area network and demonstrate how the model is able to identify known faults.