  This paper focuses on density-based clustering, particularly the Density Peak
(DP) algorithm and the one based on density-connectivity DBSCAN; and proposes a
new method which takes advantage of the individual strengths of these two
methods to yield a density-based hierarchical clustering algorithm. Our
investigation begins with formally defining the types of clusters DP and DBSCAN
are designed to detect; and then identifies the kinds of distributions that DP
and DBSCAN individually fail to detect all clusters in a dataset. These
identified weaknesses inspire us to formally define a new kind of clusters and
propose a new method called DC-HDP to overcome these weaknesses to identify
clusters with arbitrary shapes and varied densities. In addition, the new
method produces a richer clustering result in terms of hierarchy or dendrogram
for better cluster structures understanding. Our empirical evaluation results
show that DC-HDP produces the best clustering results on 14 datasets in
comparison with 7 state-of-the-art clustering algorithms.
