  Density-based clustering is the task of discovering high-density regions of
entities (clusters) that are separated from each other by contiguous regions of
low-density. DBSCAN is, arguably, the most popular density-based clustering
algorithm. However, its cluster recovery capabilities depend on the combination
of the two parameters. In this paper we present a new density-based clustering
algorithm which uses reverse nearest neighbour (RNN) and has a single
parameter. We also show that it is possible to estimate a good value for this
parameter using a clustering validity index. The RNN queries enable our
algorithm to estimate densities taking more than a single entity into account,
and to recover clusters that are not well-separated or have different
densities. Our experiments on synthetic and real-world data sets show our
proposed algorithm outperforms DBSCAN and its recent variant ISDBSCAN.
