  Identifying groups that share common features among datasets through
clustering analysis is a typical problem in many fields of science,
particularly in post-omics and systems biology research. In respect of this,
quantifying how a measure can cluster or organize intrinsic groups is important
since currently there is no statistical evaluation of how ordered is, or how
much noise is embedded in the resulting clustered vector. Many of the
literature focuses on how well the clustering algorithm orders the data, with
several measures regarding external and internal statistical measures; but none
measure has been developed to statistically quantify the noise in an arranged
vector posterior a clustering algorithm, i.e., how much of the clustering is
due to randomness. Here, we present a quantitative methodology, based on
autocorrelation, to assess this problem.
