Experiments in parallel clustering with DBSCAN. We present a new result concerning the parallelisation of DBSCAN, a Data Mining algorithm for density-based spatial clustering. The overall structure of DBSCAN has been mapped to a skeleton-structured program that performs parallel exploration of each cluster. The approach is useful to improve performance on high-dimensional data, and is general w.r.t. the spatial index structure used. We report preliminary results of the application running on a Beowulf with good efficiency