At the very early stage of finding homogeneous and distinguishable patterns from data, many different clustering algorithms can be used, providing each of them different results. Further analysis is required to select the definite clustering. In this research, we are introducing Traffic Light Panel (TLP) for the first time in this discovery process, with the aim of using it to interpret the clusters and establishing relationships between the quality of a certain clustering and the aspect of the TLP and consider the TLP a goodness-of-clustering indicator itself. In a totally non-supervised context, were no previously validated references are available, new quality criteria are defined for the TLP and used to select among several possible clusterings of the same. Our methodological approach is validated with a financial data set from Venezuelan Stock Exchange providing good results, which have been confronted and supported with posterior feedback from expert financial analysts