• 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 [1]. The stability index is the mean of the Jaccard coefficient [2] 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 [3] 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 meaning:
• There is no substantial clustering structure: [-1, 0.25].
• The clustering structure is weak and could be artificial: ]0.25, 0.50].
• There is a reasonable clustering structure: ]0.50, 0.70].
• A strong clustering structure has been found: ]0.70, 1].

[1] Cheng, R. and Milligan, G. W. (1996). Measuring the influence of individual data points in a cluster analysis. Journal of Classification, 13, 315–335.
[2] 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.
[3] Kaufman, L. and Rousseeuw, P. (1990). Finding Groups in Data: An Introduction to Cluster Analysis. Wiley.