The influence of an extreme event depends on the geographical features of the region where the event occurs. In order to understand the behavior of an extreme event, we consider statistical models capable of capturing the extremes and their spatial dependence. Max-stable processes are widely used in studying extreme events. However, assuming a fixed extremal dependence for a max-stable process may not be reasonable, depending on the topology of the region under study. In environmental extreme events, different types of extremal dependencies can appear across the spatial domain. In this study, we present an adapted spectral clustering algorithm for max-stable processes. This algorithm combines spectral clustering with extremal concurrence probability to cluster locations into $k$ regions, each with an homogeneous extremal dependence. Additionally, we propose an approach to model the entire region based on the clustered zones. In order to validate the proposed methodology, we tested it in two simulation cases using a non-stationary max-stable mixture model. The accuracy of the results encouraged us to apply it to two datasets: rainfall data on the east coast of Australia and rainfall over France.