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 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 …

A novel clustering method based on density peaks (DPC) was published on the journal
Science. However, for DPC, it is a challenge to choose an appropriate cutoff distance dc …

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 …

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 peaks clustering (DPC) algorithm plays an important role in data mining by
quickly identifying cluster centers using decision graphs to identify arbitrary clusters …

In 2014, a novel clustering algorithm called Density Peak Clustering (DPC) was proposed in
journal Science, which has received great attention in many fields due to its simplicity and …

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 …

Density peaks clustering (DPC) algorithm published in the US journal Science in 2014 is a
novel clustering algorithm based on density. It needs neither iterative process nor more …