In this paper we present the Clustering-Labels-Score Patterns Spotter (CLaSPS), a new methodology for the determination of correlations among astronomical observables in complex datasets, based on the application of distinct unsupervised clustering techniques and the use of additional information for the selection of the optimal spontaneous associations of sources in the original feature space. The novelty in this approach is the criterion followed for the selection of the optimal clusterings, based on a quantitative measure of the degree of correlation between the features used for the determination of the clusters and a set of observables, the labels, not employed for the clustering.
