This paper introduces a new semi-supervised evidential clustering algorithm. It considers label constraints and exploits the evidence theory to create a credal partition coherent with the background knowledge. The main characteristics of the new method is its ability to express the uncertainties of partial prior information by assigning each constrained object to a set of labels. It enriches previous existing algorithm that allows the preservation of the uncertainty in the constraint by adding the possibility to favor crisp decision following the inherent structure of the dataset. The advantages of the proposed approach are illustrated using both a synthetic dataset and a real genomics dataset.