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Inferring the quality of service experienced by wireless users is challenging, as network monitoring does not capture the service perception for each user individually. In this paper, we propose an unsupervised machine learning approach to infer the quality of service experienced by wireless users, based on the different usage profiles of a large-scale wireless network. To this end, our approach correlates the usage data of access points, and the summaries of connection flows passing through the access points in the network. Then, we apply the k-means clustering algorithm to infer different network usage profiles. We evaluate our proposed approach to infer QoS on a real large-scale wireless network, and the results show that discriminating the flows into five clusters allows identifying prevalent usage profiles of the degraded state of the network and overload conditions in access points, considering only the flow summaries. [ABSTRACT FROM AUTHOR]