Constrained clustering has recently become an active research topic. This type of clustering methods takes advantage of partial knowledge in the form of pairwise constraints, and acquires significant improvement beyond the traditional un-supervised clustering. However, most of the existing constrained clustering methods use constraints which are selected at random. Recently active constrained clustering algorithms utilizing active constraints have proved themselves to be more effective and efficient. In this paper, we propose an improved algorithm which introduces multiple representatives into constrained clustering to make further use of the active constraints. Experiments on several benchmark data sets and public image data sets demonstrate the advantages of our algorithm over the referenced competitors.