In this paper we analyze the performance of a novel clustering objective that optimizes a neural network to predict segmentation. We challenge the reported results by replicating the original experiments and conducting additional tests to gain an insight into the algorithm. We analyzed the efficiency of the clustering objective on a different architecture, dataset and hyper-parameters. To our surprise the algorithm demonstrated considerably lower results when running on the new setup. Further, in our work we detail the reasons behind the discrepancy and provide configurations under which the method performs best. We show that the objective is highly sensitive to the type of images it is predicting and the complexity of the architecture that is being used with.