Regression tests often result in many deviations (differences between two system versions), either due to changes or regression faults. Many deviations can be caused by the same change or regression fault. In order for the tester to analyze such deviations efficiently, it would be helpful to accurately group them, such that each group contains deviations representing one unique change or regression fault. We investigate the use of clustering, based on database manipulations and test specifications (from test models), to group regression test deviations according to the faults or changes causing them. We also propose assessment criteria based on the concept of entropy to compare alternative clustering strategies. In the context of database applications, our results show that our clustering approach can indeed serve as an accurate strategy for grouping regression test deviations according to the faults and changes that caused them. Among the four test campaigns assessed, deviations were clustered perfectly for two of them, while for the two others, clusters were all homogenous. Our analysis suggests that this can significantly reduce the effort spent by testers in analyzing regression test deviations, increase their level of confidence under time constraints, and therefore make regression testing more scalable.