The aim of online clustering is to discover a structure in running data. Adding label constraints or pairwise constraints to this has shown to improve the clustering accuracy. In this study we present an analysis of how different hyperparameters – proportion of constraints, initial number of clusters, and batch window size – affect most recent and popular online constrained clustering methods, using three different metrics. Our results show that initial number of clusters and window size have an effect on clustering results, while the proportion of constraints does not. We also demonstrate that online clustering performs better than clustering of the whole data together. Our overall findings point at the need for new, more effective online constrained clustering methods.