  Clustering consists of a popular set of techniques used to separate data into
interesting groups for further analysis. Many data sources on which clustering
is performed are well-known to contain random and systematic measurement
errors. Such errors may adversely affect clustering. While several techniques
have been developed to deal with this problem, little is known about the
effectiveness of these solutions. Moreover, no work to-date has examined the
effect of systematic errors on clustering solutions.
  In this paper, we perform a Monte Carlo study to investigate the sensitivity
of two common clustering algorithms, GMMs with merging and DBSCAN, to random
and systematic error. We find that measurement error is particularly
problematic when it is systematic and when it affects all variables in the
dataset. For the conditions considered here, we also find that the
partition-based GMM with merged components is less sensitive to measurement
error than the density-based DBSCAN procedure.
