The density peak clustering algorithm (DPC) has poor accuracy when clustering datasets
with large density differences among clusters, and the assignment strategy may cause …

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 …

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 …

In order to solve the problem of manual selection of cluster centers in density peaks
clustering algorithm, an automatic selection algorithm of cluster centers was proposed in this …

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 …

Density peaks clustering algorithm is a simple but effective clustering method, which
requires fewer parameters and iteration, and can determine the number of clusters. But this …

Density peak clustering (DPC) algorithm has become a well-known clustering method
during the last decade, The research communities believe that DPC is a powerful tool …

In order to solve the problem that the density peak clustering algorithm (FDP) needs to
manually selected the center on the decision graph, an integration algorithm based on FDP …