Analysis of movement behaviour of individuals has emerged as relevant research field and a wide range of potential applications have been proposed in previous literature. The advancement of positioning technologies and the development of hardware and software have contributed to the popularization of mobile devices and the expansion of Location Based Services. One of the consequences is the increase of mobility data available for developing new methods of analysis of movement behaviour. Previous research on GPS data has mainly focused on trajectory analysis, although alternative approaches propose considering only the stationary parts. Some of these works aim to discover the places visited by the user and the stays performed on them as first step for a user-s movement analysis. Clustering based approaches rely on different algorithms for clustering GPS logs collected by the user. A general approach suitable for movement behaviour analysis is suggested. The aims of this general approach are detecting the places visited by a user as well as characterising the stays at these places and the transitions performed between them. In order to detect the visited places, three spatio-temporal clustering approaches are proposed and evaluated under a common evaluation framework. This framework includes spatial a temporal measures to systematically assess three algorithms performing incremental, density-based clustering and a combination of both. Ground truth data collected by four users and tagged during collection process is used to test the validity of the approaches. The optimum parameter values for the algorithms are determined according to the results of the quality evaluation. The characterisation of the user stays and transitions implies the extraction of them as well as the evaluation of this extraction comparing the three clustering algorithms. Two indices related with number and duration of stays and transitions are suggested for the assessment of the extraction accuracy. A movement behaviour profile of a user is developed and described.