  In this work, we investigate the current flaws with identifying
network-related errors, and examine how K-Means and Long-Short Term Memory
Networks solve these problems. We demonstrate that K-Means is able to classify
messages, but not necessary provide meaningful clusters. However, Long-Short
Term Memory Networks are able to meet our goals of providing an intelligent
clustering of messages by grouping messages that are temporally related.
Additionally, Long-Short Term Memory Networks can provide the ability to
understand and visualize temporal causality, which unlocks the ability to warn
about errors before they happen. We show that LSTMs have a 70% accuracy on
classifying network errors, and provide some suggestions on future work.
