The DBSCAN algorithm views clusters as areas of high density separated by areas of low density. Due to this rather generic view, clusters found by DBSCAN can be any shape, as opposed to k-means which assumes that clusters are convex shaped. Numerical features should use standard rescaling.
Maximum distance to consider two samples in the same neighborhood.
Minimum ratio of records in its neighborhood for a point to be considered as a core point. If set to a higher value, DBSCAN will find denser clusters, whereas if it is set to a lower value, the found clusters will be more sparse.