We present a novel metric for generative modeling evaluation that uses divergence between dendrograms computed from training and generated data. Our approach, which borrows theoretical foundations from clustering analysis, is validated by sampling from real datasets and also on samples generated by a GAN during training, with results comparable to state-of-the-art metrics.