This is related to what I am doing recently. Essentially, clustering statistics means consider the relationship between more than one point (Histogram is one example). A good example is 2-Point Correlation Function, which consider the statistics of edges, not points.

The following articles might be good start points:

Survey

Vicent J. Martinez, Enn Saar
Clustering statistics in cosmology
In SPIE Proceedings Vol. 4847, 2002, "Astronomical Data Analysis II."
http://arxiv.org/abs/astro-ph/0209208

Nicholas M. Ball, Robert J. Brunner
Data Mining and Machine Learning in Astronomy
Int.J.Mod.Phys.D19:1049-1106,2010
http://arxiv.org/abs/0906.2173

Alexander S. Szalay, Jim Gray, Jan vandenBerg
Petabyte Scale Data Mining: Dream or Reality?
In SIPE Astronmy Telescopes and Instruments, 22-28 August 2002, Waikoloa, Hawaii
http://arxiv.org/abs/cs/0208013

Fast Statistics Computation

Joshua Dolence, Robert Brunner
Fast two-point correlations of extremely large data sets
9th LCI International Conference on HighPerformance Clustered Computing (2008)

Jeffrey P. Gardner, Andrew Connolly, Cameron McBride
Enabling Rapid Development of Parallel Tree Search Applications
In Challenges of Large Applications in Distributed Environments (CLADE) 2007, Monterey, CA.
http://arxiv.org/abs/0709.1967

Andrew Moore (CMU), Andy Connolly (UPitt), Chris Genovese (CMU), Alex Gray (CMU), Larry Grone (CMU), Nick Kanidoris II (CMU), Robert Nichol (CMU), Jeff Schneider (CMU), Alex Szalay (JHU), Istvan Szapudi (CITA), Larry Wasserman (CMU)
Fast Algorithms and Efficient Statistics: N-point Correlation Functions
In Proceedings of MPA/MPE/ESO Conference "Mining the Sky", July 31 - August 4, 2000, Garching, Germany