Correlations: This analysis finds which pairs of metrics are
representing equivalent facets of the datasets. We obtain the Pearson
correlation coefficient. The score is in the range [-1,1].
Perfect correlations: -1 (negative), and 1 (positive)
Stability: This analysis permits to estimate whether the
clustering is meaningfully affected by small variations in the data
. The stability index is the mean of the Jaccard coefficient 
values of 1000 bootstrap replicates. The values are in the range
[0,1], having the following meaning:
Unstable: [0, 0.60[
Doubtful: [0.60, 0.75]
Stable: ]0.75, 0.85]
Highly Stable: ]0.85, 1]
Goodness of classifications: The goodness of the
classifications are assessed by validating the clusters generated. For
this purpose, we use the Silhouette width as validity index. Kaufman
and Rousseeuw  suggested the interpretation of the global
Silhouette width score as the effectiveness of the clustering
structure. The values are in the range [0,1], having the following
There is no substantial clustering structure: [-1, 0.25].
The clustering structure is weak and could be artificial: ]0.25,
There is a reasonable clustering structure: ]0.50, 0.70].
A strong clustering structure has been found: ]0.70, 1].
 Cheng, R. and Milligan, G. W. (1996). Measuring the influence of
individual data points in a cluster analysis. Journal of Classification,
 Jaccard, C. (1901). Distribution de la flore alpine dans le Basin de
Dranses et dans quelques regions voisines. Bulletin de la Societe Vaudoise
des Sciences Naturelles, 37, 241–272.
 Kaufman, L. and Rousseeuw, P. (1990). Finding Groups in Data: An
Introduction to Cluster Analysis. Wiley.