  Density peaks clustering (DP) has the ability of detecting clusters of
arbitrary shape and clustering non-Euclidean space data, but its quadratic
complexity in both computing and storage makes it difficult to scale for big
data. Various approaches have been proposed in this regard, including MapReduce
based distribution computing, multi-core parallelism, presentation
transformation (e.g., kd-tree, Z-value), granular computing, and so forth.
However, most of these existing methods face two limitations. One is their
target datasets are mostly constrained to be in Euclidian space, the other is
they emphasize only on local neighbors while ignoring global data distribution
due to restriction to cut-off kernel when computing density. To address the two
issues, we present a faithful and parallel DP method that makes use of two
types of vector-like distance matrices and an inverse leading-node-finding
policy. The method is implemented on a message passing interface (MPI) system.
Extensive experiments showed that our method is capable of clustering
non-Euclidean data such as in community detection, while outperforming the
state-of-the-art counterpart methods in accuracy when clustering large
Euclidean data. Our code is publicly available at
https://github.com/alanxuji/FaithPDP.
