  Recent advancements in neutron and X-ray sources, instrumentation and data
collection modes have significantly increased the experimental data size (which
could easily contain 10$^{8}$ -- 10$^{10}$ data points), so that conventional
volumetric visualization approaches become inefficient for both still imaging
and interactive OpenGL rendition in a 3D setting. We introduce a new approach
based on the unsupervised machine learning algorithm, Density-Based Spatial
Clustering of Applications with Noise (DBSCAN), to efficiently analyze and
visualize large volumetric datasets. Here we present two examples of analyzing
and visualizing datasets from the diffuse scattering experiment of a single
crystal sample and the tomographic reconstruction of a neutron scanning of a
turbine blade. We found that by using the intensity as the weighting factor in
the clustering process, DBSCAN becomes very effective in de-noising and
feature/boundary detection, and thus enables better visualization of the
hierarchical internal structures of the neutron scattering data.
