This research proposes the analysis and subsequent characterisation of Android malware families by means of low
dimensional visualisations using dimensional reduction techniques. The well-known Malgenome data set, coming from
the Android Malware Genome Project, has been thoroughly analysed through the following six dimensionality reduction
techniques: Principal Component Analysis, Maximum Likelihood Hebbian Learning, Cooperative Maximum Likelihood
Hebbian Learning, Curvilinear Component Analysis, Isomap and Self Organizing Map. Results obtained enable a clear visual
analysis of the structure of this high-dimensionality data set, letting us gain deep knowledge about the nature of such Android
malware families. Interesting conclusions are obtained from the real-life data set under analysis.
