Active clustering is a technique that groups data into homogeneous clusters through interactions with the user, who provides feedback in the form of constraints. The quality of the feedback, and therefore of the clustering, depends on the user's knowledge. In this study, explainable artificial intelligence (XAI) methods are employed to enhance the interaction with active clustering by providing additional information to the user. We implemented this approach with COBRA, an active clustering algorithm, and evaluated it on various datasets. Our experimental results show that the information generated by XAI methods assists a simulated user in better guiding the clustering process towards results that are more faithful to the ground truth compared to situations where such information is not available.