The manual selection of threshold dc and cluster centers are the big limitations of the
clustering by fast search and find of density peaks algorithm (DPC). In this paper, the data …

The density peak clustering algorithm (DPC) has been widely concerned by researchers
since it was proposed. Its advantage lies in its ability to achieve efficient clustering based on …

Density peaks clustering algorithms calculate the local density based on the cutoff distance
and the global distribution of the sample. They cannot capture the local characteristics of the …

Density Peaks Clustering (DPC) tries to use two objectives: density and peaks, to
automatically determine the number of clusters. It is claimed to be applicable to data sets …

Density peak clustering algorithm is a new algorithm to achieve fast clustering by finding
density peak. It has the advantages of simple implementation, less parameters required …

Abstract Density Peak Clustering (DPC) was proposed in the journal Science in 2014 and
has been widely applied in many fields due to its simplicity and effectiveness. However …

Density Peaks Clustering is a novel clustering algorithm, which can find clusters of arbitrary
shapes with fast speed. However, it has a few disadvantages, for example, when the data …

Clustering by fast search and finding of Density Peaks (called as DPC) introduced by Alex
Rodr í guez and Alessandro Laio attracted much attention in the field of pattern recognition …

Clustering by fast search and find of Density Peaks (DPC) is a famous clustering algorithm.
The main advantages of DPC are that its clustering process is iteration-free and it is …

The Density Peak Clustering (DPC) algorithm is a new density-based clustering method. It
spends most of its execution time on calculating the local density and the separation …