Abstract
Motivated by a real problem of tourism destination and regional studies, we develop a clustering algorithm to identify similar patterns of tourism time series. The algorithm joins the copula–approach to cluster analysis with the fuzzy methodology, allowing to extend usual clustering methods for time series based on Pearson’s correlation and to capture the uncertainty that arises assigning units to clusters.