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Since the terahertz frequency band (0.1–1 THz) has attracted considerable attention for the upcoming sixth-generation (6G) wireless communication systems, accurate models for multipath propagation in this frequency range need to be established. Such models advantageously use the fact that multi-path components (MPCs) occur typically in clusters, i.e., groups of MPCs that have similar delays and angles. In this paper, we first analyze the limitations of a widely used clustering algorithm, Kernel-Power-Density (KPD), in evaluating an extensive THz outdoor measurement campaign at 145–146 GHz, particularly its inability to detect small clusters. We introduce a modified version, which we term multi-level KPD (ML-KPD), iteratively applying KPD to detect whether a cluster determined in the previous round is made up of multiple clusters. We first apply the method to synthetic channels to demonstrate its efficacy and select suitable values for the adaptive hyperparameters. Then, multi-level KPD is applied to our channel measurements in line-of-sight (LOS) and non-line-of-sight (NLOS) environments to determine statistics for the number of clusters and the cluster spreads.