  It is demonstrated how linear computational time and storage efficient
approaches can be adopted when analyzing very large data sets. More
importantly, interpretation is aided and furthermore, basic processing is
easily supported. Such basic processing can be the use of supplementary, i.e.
contextual, elements, or particular associations. Furthermore pixellated grid
cell contents can be utilized as a basic form of imposed clustering. For a
given resolution level, here related to an associated m-adic ($m$ here is a
non-prime integer) or p-adic ($p$ is prime) number system encoding, such
pixellated mapping results in partitioning. The association of a range of
m-adic and p-adic representations leads naturally to an imposed hierarchical
clustering, with partition levels corresponding to the m-adic-based and
p-adic-based representations and displays. In these clustering embedding and
imposed cluster structures, some analytical visualization and search
applications are described
