Abstract Density Peak (DPeak) clustering algorithm is not applicable for large scale data,
due to two quantities, ie, ρ and δ, are both obtained by brute force algorithm with complexity …

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 …

Fast and efficient are common requirements for all clustering algorithms. Density peaks
clustering algorithm (DPC) can deal with non-spherical clusters well. However, due to the …

An efficient clustering algorithm by fast search and find of density peaks (DP) was proposed
and attracted much attention from researchers. It assumes that cluster centers are …

With the rapid development of information technology, massive amount of data is generated.
How to discover useful information to support decision-making has become one of the …

Density peaks is a popular clustering algorithm, used for many different applications,
especially for non-spherical data. Although powerful, its use is limited by quadratic time …

Recently a density peaks based clustering algorithm (dubbed as DPC) was proposed to
group data by setting up a decision graph and finding out cluster centers from the graph fast …

Density peaks clustering (DPC) has had an impact in many fields, as it can quickly select
centers and effectively process complex data. However, it also has low operational efficiency …

Recently, a new density-peak-based clustering method, called clustering with local density
peaks-based minimum spanning tree (LDP-MST), was proposed, which has several …

Clustering by fast search and find of density peaks (CFSFDP) is a state-of-the-art density-
based clustering algorithm that can effectively find clusters with arbitrary shapes. However, it …