  Clustering by fast search and find of density peaks (DPC) (Since, 2014) has
been proven to be a promising clustering approach that efficiently discovers
the centers of clusters by finding the density peaks. The accuracy of DPC
depends on the cutoff distance ($d_c$), the cluster number ($k$) and the
selection of the centers of clusters. Moreover, the final allocation strategy
is sensitive and has poor fault tolerance. The shortcomings above make the
algorithm sensitive to parameters and only applicable for some specific
datasets. To overcome the limitations of DPC, this paper presents an improved
probability propagation algorithm for density peak clustering based on the
natural nearest neighborhood (DPC-PPNNN). By introducing the idea of natural
nearest neighborhood and probability propagation, DPC-PPNNN realizes the
nonparametric clustering process and makes the algorithm applicable for more
complex datasets. In experiments on several datasets, DPC-PPNNN is shown to
outperform DPC, K-means and DBSCAN.
