  Star clusters are often hard to find, as they may lie in a dense field of
background objects or, because in the case of embedded clusters, they are
surrounded by a more dispersed population of young stars. This paper discusses
four algorithms that have been developed to identify clusters as stellar
density enhancements in a field, namely stellar density maps from star counts,
the neareast neighbour method and the Voronoi tessellation, and the separation
of minimum spanning trees. These methods are tested and compared to each other
by applying them to artificial clusters of different sizes and morphologies.
While distinct centrally concentrated clusters are detected by all methods,
clusters with low overdensity or highly hierarchical structure are only
reliably detected by methods with inherent smoothing (star counts and nearest
neighbour method). Furthermore, the algorithms differ strongly in computation
time and additional parameters they provide. Therefore, the method to choose
primarily depends on the size and character of the investigated area and the
purpose of the study.
