  Counts of attribute-value combinations are central to the profiling of a
dataset, particularly in determining fitness for use and in eliminating bias
and unfairness. While counts of individual attribute values may be stored in
some dataset profiles, there are too many combinations of attributes for it to
be practical to store counts for each combination. In this paper, we develop
the notion of storing a "label" of limited size that can be used to obtain good
estimates for these counts. A label, in this paper, contains information
regarding the count of selected patterns--attributes values combinations--in
the data. We define an estimation function, that uses this label to estimate
the count of every pattern. We present the problem of finding the optimal label
given a bound on its size and propose a heuristic algorithm for generating
optimal labels. We experimentally show the accuracy of count estimates derived
from the resulting labels and the efficiency of our algorithm.
