  This work emphasizes that heterogeneity, diversity, discontinuity, and
discreteness in data is to be exploited in classification and regression
problems. A global a priori model may not be desirable. For data analytics in
cosmology, this is motivated by the variety of cosmological objects such as
elliptical, spiral, active, and merging galaxies at a wide range of redshifts.
Our aim is matching and similarity-based analytics that takes account of
discrete relationships in the data. The information structure of the data is
represented by a hierarchy or tree where the branch structure, rather than just
the proximity, is important. The representation is related to p-adic number
theory. The clustering or binning of the data values, related to the precision
of the measurements, has a central role in this methodology. If used for
regression, our approach is a method of cluster-wise regression, generalizing
nearest neighbour regression. Both to exemplify this analytics approach, and to
demonstrate computational benefits, we address the well-known photometric
redshift or `photo-z' problem, seeking to match Sloan Digital Sky Survey (SDSS)
spectroscopic and photometric redshifts.
