  In the last two decades several biclustering methods have been developed as
new unsupervised learning techniques to simultaneously cluster rows and columns
of a data matrix. These algorithms play a central role in contemporary machine
learning and in many applications, e.g. to computational biology and
bioinformatics. The H-score is the evaluation score underlying the seminal
biclustering algorithm by Cheng and Church, as well as many other subsequent
biclustering methods. In this paper, we characterize a potentially troublesome
bias in this score, that can distort biclustering results. We prove, both
analytically and by simulation, that the average H-score increases with the
number of rows/columns in a bicluster. This makes the H-score, and hence all
algorithms based on it, biased towards small clusters. Based on our analytical
proof, we are able to provide a straightforward way to correct this bias,
allowing users to accurately compare biclusters.
