  The application of unsupervised learning approaches, and in particular of
clustering techniques, represents a powerful exploration means for the analysis
of network measurements. Discovering underlying data characteristics, grouping
similar measurements together, and identifying eventual patterns of interest
are some of the applications which can be tackled through clustering. Being
unsupervised, clustering does not always provide precise and clear insight into
the produced output, especially when the input data structure and distribution
are complex and difficult to grasp. In this paper we introduce EXPLAIN-IT, a
methodology which deals with unlabeled data, creates meaningful clusters, and
suggests an explanation to the clustering results for the end-user. EXPLAIN-IT
relies on a novel explainable Artificial Intelligence (AI) approach, which
allows to understand the reasons leading to a particular decision of a
supervised learning-based model, additionally extending its application to the
unsupervised learning domain. We apply EXPLAIN-IT to the problem of YouTube
video quality classification under encrypted traffic scenarios, showing
promising results.
