  The data preprocessing stage is crucial in clustering. Features may describe
entities using different scales. To rectify this, one usually applies feature
normalisation aiming at rescaling features so that none of them overpowers the
others in the objective function of the selected clustering algorithm. In this
paper, we argue that the rescaling procedure should not treat all features
identically. Instead, it should favour the features that are more meaningful
for clustering. With this in mind, we introduce a feature rescaling method that
takes into account the within-cluster degree of relevance of each feature. Our
comprehensive simulation study, carried out on real and synthetic data, with
and without noise features, clearly demonstrates that clustering methods that
use the proposed data normalization strategy clearly outperform those that use
traditional data normalization.
