The clustering validation and clustering interpretation are the two last steps of clustering process. The validation step permits to evaluate the goodness of clustering results using some measures. Valid results are then generally interpreted and used in cluster analysis. The validity measures are classified into three categories: unsupervised measures, supervised measures and relative measures. Several supervised measures have been proposed to perform supervised evaluation such as <i>entropy, purity, F-measure, Jaccard coefficient</i> and <i>Rand statistic</i>. Generally, these measures evaluate results according to class labels. However, they are not always able to distinguish interpretable clusters because most of them depends on the number of labels. This paper proposes a new supervised evaluation measure - called "homogeneity degree"- that permits to merge the steps of validation and interpretation. Our measure is applied to a real traffic data set and is used to interpret some traffic situations. Comparison with other evaluation measures shows the performance of our proposal.