Since movements can be tracked easier by the help of GPS-sensors, the quantity of movement data rises rapidly. These can potentially exhibit many behavioural patterns which provide valuable insights for investigation or protection of moving objects. In order to find such patterns, the unstructered data must be processed and visualised. This enables their following analysis. To increase the transparency of this process and to support the user in the analysis, the opportunities of interaction are an important issue. So far, interactive actions for the visual analysis of movement data are pared down to a minimum and are used only for the exploration of the results of the processing. That is why, in this thesis, we develop a framework which firmly connects automatic data processing through interactive methods with visualisation in order to improve the capabilities of analysis and interaction. Therefore, a partition method, a cluster algorithm, and an adjustable distance function are combined by a continuous interactive visualisation in order to support analysts towards the understanding and directing of the extraction of patterns and the concluding analysis. To show what makes this approach distinguishable from existing approaches, the utilised components will be explained in detail, innovations will be illustrated, and advantages and disadvantages will be explained.