For low-voltage distribution networks (LVDNs), accurate models depicting network and phase connectivity are crucial to the analysis, planning, and operation of these networks. However, phase connectivity data in the LVDN are usually incorrect or missing. Wrong or incomplete phase information collected could lead to unbalanced operation of three-phase distribution systems and increased power loss. Based on the advanced measurement infrastructure (AMI) in the development of smart grids, in this study, a novel data-driven phase identification algorithm is proposed. Firstly, the method involves extracting features from voltage–time matrices using a non-linear dimension reduction algorithm. Secondly, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is used to divide customers into clusters with arbitrary shape. Finally, the algorithms were tested with the IEEE European Low Voltage Test Feeder of the IEEE PES AMPS DSAS Test Feeder working group. The results showed an accuracy of over 90% for the method.