  Star cluster studies hold the key to understanding star formation, stellar
evolution, and origin of galaxies. The detection and characterization of
clusters depend on the underlying background density and the cluster richness.
We examine the ability of the Parzen Density Estimation (a.k.a. Parzen Windows)
method, which is a generalization of the well-known Star Count method, to
detect clusters and measure their properties. We apply it on a range of
simulated and real star fields, considering square and circular windows, with
and without Gaussian kernel smoothing. Our method successfully identifies
clusters and we suggest an optimal standard deviation of the Gaussian Parzen
window for obtaining the best estimates of these parameters. Finally, we
demonstrate that the Parzen Windows with Gaussian kernels are able to detect
small clusters in regions of relatively high background density where the Star
Count method fails.
