Tracking a person, an animal, or a vehicle generates a vast amount of spatio-temporal data, that has to be processed and analyzed differently from ordinary data generally used in knowledge discovery. This paper presents existing spatiotemporal clustering algorithms, suitable for such data and compares their running time, noise sensitivity, quality of results and the ability to discover clusters according to nonspatial, spatial and temporal values of the objects.