  We study a data-driven approach to the bee identification problem for DNA
strands. The bee-identification problem, introduced by Tandon et al. (2019),
requires one to identify $M$ bees, each tagged by a unique barcode, via a set
of $M$ noisy measurements. Later, Chrisnata et al. (2022) extended the model to
case where one observes $N$ noisy measurements of each bee, and applied the
model to address the unordered nature of DNA storage systems. In such systems,
a unique address is typically prepended to each DNA data block to form a DNA
strand, but the address may possibly be corrupted. While clustering is usually
used to identify the address of a DNA strand, this requires $\mathcal{M}^2$
data comparisons (when $\mathcal{M}$ is the number of reads). In contrast, the
approach of Chrisnata et al. (2022) avoids data comparisons completely. In this
work, we study an intermediate, data-driven approach to this identification
task. For the binary erasure channel, we first show that we can almost surely
correctly identify all DNA strands under certain mild assumptions. Then we
propose a data-driven pruning procedure and demonstrate that on average the
procedure uses only a fraction of $\mathcal{M}^2$ data comparisons.
Specifically, for $\mathcal{M}= 2^n$ and erasure probability $p$, the expected
number of data comparisons performed by the procedure is $\kappa\mathcal{M}^2$,
where $\left(\frac{1+2p-p^2}{2}\right)^n \leq \kappa \leq
\left(\frac{1+p}{2}\right)^n $.
