Atrial flutter (AFl) is a common heart rhythm disorder driven by different self-sustaining electrophysiological atrial mechanisms. In this work, we tried to automatically distinguish the macro-mechanism sustaining the arrhythmia in an individual patient using the non-invasive 12-lead electrocardiogram (ECG). We implemented a concurrent clustering and classification algorithm (CCC) to discriminate the clinical classes and look for potential similarities between patient features in each class, thus suggesting that these patients would require a similar treatment. The CCC performance was then compared to a standard supervised technique (K-nearest neighbor, KNN). 3-class classification (macro-reentry right atrium, macro-reentry left atrium, and others) achieved 48.3% and 72.0% CCC and KNN accuracy, respectively. 4-class classification (tricuspidal reentry, mitral reentry, fig-8 macro-reentry, and others) achieved 41.6% and 71.2% CCC and KNN accuracy, respectively. Our results show that a clustering approach does not improve the performance of AFl classification because the semi-supervised method leads to clusters that are strongly overlapping between the different ground truth classes. In contrast, the supervised learning approach shows potential for the classification, although constrained by the complexity and the multiple variables that influence the underlying mechanisms.