Diffusion MRI tractography produces massive sets of streamlines that need to be clustered into anatomically meaningful bundles. Conventional clustering techniques group streamlines based on their proximity in Euclidean space. We have developed an unsupervised method for clustering tractography streamlines based on their neighboring anatomical structures, rather than their coordinates in Euclidean space. In this work, we show how this approach can be extended to find corresponding clusters across subjects without inter-subject registration. We evaluate the approach on data from the MGH-Harvard-USC Lifespan Human Connectome Project, showing improved correspondence in tract clusters across subjects aged 8-90, without the need for registration.