This master’s thesis focuses on spatio-temporal data mining, a specialized field of data mining and spatial analysis that aims at discovering interest- ing patterns, relationships, and insights from data that have both spatial and temporal dimensions. These relationships are useful to help in a wide spectrum of applications and contexts, from urban planning to epidemiology. Among different approaches to address spatio-temporal data mining we fo- cus on co-location pattern mining. This method tries to uncover correlation between features or attributes of a dataset whose instances are usually found together in the same geographic area and at the same time. For example if we consider a dataset that represents events that may happen in a urban context, we may find that an episode of type "Traffic-congestion" is often spatially and temporally close to an episode of type "Sport-event".