Unsupervised learning is becoming an essential tool to analyze the increasingly large
amounts of data produced by atomistic and molecular simulations, in material science, solid …

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 …

Clustering by fast search and find of density peaks (DPC) is a new clustering method that
was reported in Science in June 2014. This clustering algorithm is based on the assumption …

Uneven density data refers to data with a certain difference in sample density between
clusters. The local density of density peaks clustering algorithm (DPC) does not consider the …

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 …

It is crucial to provide compatible treatment schemes for a disease according to various
symptoms at different stages. However, most classification methods might be ineffective in …

Density peaks clustering (DPC) algorithm is an efficient and simple clustering method
attracting the attention of many researchers. However, its strategy of assigning each non …

Density peaks clustering (DPC) is a novel density-based clustering algorithm that identifies
center points quickly through a decision graph and assigns corresponding labels to …

Density peaks clustering algorithm (DP) has difficulty in clustering large-scale data, because
it requires the distance matrix to compute the density and-distance for each object, which …