… of the fuzzy distances between a data point and its neighbors. As a fuzzy variant of DPC,
a novel fuzzy density peaks clustering (FDPC) method FDPC based on fuzzy operators (…

… in sample density between clusters. The local density of density peaks clustering algorithm
(… the effect of sample density difference between clusters of uneven density data, which may …

… clustering method known as Density Peaks Clustering based on Density Backbones and
Fuzzy … main steps: (1) identifying cluster centers, (2) forming cluster backbones, and (3) label …

… , in this study, the fuzzy neighborhood- density peaks(FN-DP) clustering which integrates
the speed of DP clustering algorithm with the robustness of FJP algorithm is proposed. …

… into the density peaks clustering algorithm for clustering data. … use fuzzy neighborhood
relation to redefine the local density. … number of clusters, we develop an automatic cluster center …

… with a higher local density. We refer to this algorithm as DPC (Density Peak Clustering) in this
… cluster centers (ie, the density peaks) and assign the remaining points to their appropriate …

… Thus, this study proposes a novel method for density peaks clustering based on fuzzy … a
fuzzy point with the form of the fuzzy semantic cell model. Based on this model, a local density …

… fuzzy-CFSFDP for adaptive selection of the center clusters. Fuzzy-CFSFDP finds all density
peaks and treats each peak as local cluster and then merges local clusters to find the global …

… Density peak clustering (DPC) is an effective clustering … , which can significantly impact
the clustering outcome; (b) DPC … local densities; (c) it employs a crisp kernel for density …

… It is easy to generate problems such as multiple clustering … and DPC (Clustering by fast search
and find of density peaks) … of clusters, and then FCM algorithm is used to realize clustering…