  A new method for binning a set of $n$ data values into a set of m bins for
the case where the bins are of different sizes is proposed. The method skips
binning using a binary search across the bins all the time. It is proven the
method exhibits a linear average-case computation time. The experiments'
results show a speedup factor of over four compared to binning by binary search
alone for data values with unknown distributions. This result is consistent
with the analysis of the method.
