Hierarchical clustering is widely used in data mining. The single linkage criterion is powerful, as it allows for handling various shapes and densities, but it is sensitive to noise 1 . Two improvements are proposed in this work to deal with noise. First, the single linkage criterion takes into account the local density to make sure the distance involves core points of each group. Second, the hierarchical algorithm forbids the merging of representative clusters, higher than a minimum size, once identified. The experiments include a sensitivity analysis to the parameters and a comparison of the available criteria using datasets known in the literature. The latter proved that local criteria yield better results than global ones. Then, the three single linkage criteria were compared in more challenging situations that highlighted the complementariness between the two levels of improvement: the criterion and the clustering algorithm.