The problem of assessing the reliability of clusters patients identified by clustering algorithms is crucial to estimate
the significance of subclasses of diseases detectable at bio-molecular level, and more in general to support bio-medical discovery
of patterns in gene expression data.
In this paper we present an experimental analysis of the reliability of clusters discovered in lung tumor patients
using DNA microarray data.
In particular we investigate if subclasses of lung adenocarcinoma can be detected with high reliability
at bio-molecular level.
To this end we apply cluster validity measures based on random projections recently proposed by Bertoni and coworkers.
The results show that at least two subclasses of lung adenocarcinoma can be detected with relatively high reliability,
confirming and extending previous findings reported in the literature.