  Finding meaningful clusters in drive-by-download malware data is a
particularly difficult task. Malware data tends to contain overlapping clusters
with wide variations of cardinality. This happens because there can be
considerable similarity between malware samples (some are even said to belong
to the same family), and these tend to appear in bursts. Clustering algorithms
are usually applied to normalised data sets. However, the process of
normalisation aims at setting features with different range values to have a
similar contribution to the clustering. It does not favour more meaningful
features over those that are less meaningful, an effect one should perhaps
expect of the data pre-processing stage.
  In this paper we introduce a method to deal precisely with the problem above.
This is an iterative data pre-processing method capable of aiding to increase
the separation between clusters. It does so by calculating the within-cluster
degree of relevance of each feature, and then it uses these as a data rescaling
factor. By repeating this until convergence our malware data was separated in
clear clusters, leading to a higher average silhouette width.
