# Density Peaks Clustering Algorithm Based on a Divergence Distance and Tissue--Like P System
## 1 Introduction
In addition, unlike other density-based clustering methods (including MeanShift [20] and DBSCAN), it does not require any iterative step, and assigns labels to non-central points in one step. Although DPC has the advantages of simple and fast calculations, it still has the following shortcomings. On the one hand, while processing data with an uneven density distribution, DPC finds it challenging to identify clustering centers in decision graphs comprised of density and distance. On the other hand, when a point is wrongly allocated during the assignment process, the other points connected to it are also impacted, which causes a chain reaction. Many DPC variations have been suggested as solutions to the aforementioned problems. Three categories may be used to group together the enhancements of the original DPC. The first improvement aims at improving the calculation of local density. The major goal of the proposed kernels is to lessen the DPC's sensitivity to the parameter's value. As proof, [PERSON] et al. first incorporated K-nearest neighbors (KNN) into DPC to determine the local density of data points (KNN-DPC) [21]. Additionally, dimensional reduction is pre-processed using principal component analysis (PCA) when working with high-dimensional datasets to lessen computing complexity. Different from KNN-DPC, [PERSON] et al. proposed shared-nearest-neighbor-based clustering to fast search for and find density peaks (SNN-DPC) [22]. SNN-DPC enhances local density computation by using novel definitions such as SNN similarity. In [23], the K nearest Shannon entropy-based density peaks clustering algorithm named SC was proposed. To determine the local density, K-nearest neighbors and entropy values are used, which takes into account the global and local structure of the dataset. The second improvement aims to select clustering centers. Picking clustering centers may be improved in two ways: automatically selecting centers and boosting center identification in decision graphs. For instance, a single linkage density peaks clustering algorithm entitled DPSLC [24] was proposed to automatically detect all possible centers. The suggested approach leverages the neighborhood radius to choose a collection of potential density peaks that are far from their nearest higher-density spots. [PERSON] et al. [25] proposed a method for clustering graph adaptive density peaks for automatic center selection. The clustering centers in this approach are automatically chosen according to the turning angle and graph connectivity. The third improvement seeks to provide effective label assignment strategies. [PERSON], [PERSON] [26] and colleagues suggested enhanced density peaks clustering (IDPC) that uses a two-step approach to find complicated clusters. Nevertheless, this method has a high sensitivity to the parameters. Recently, the local structure of the dataset has been considered by researchers of the recently introduced DPC-DLP [27] approach, while constructing their label propagation strategy. In this methodology, the border points are given lower density values than the core points, while the core points are given greater density values. By joining each point to its k-nearest neighbors, DPC-DLP builds a graph. This graph is utilized to create cluster backbones, and, after that, all data points receive the labels from the backbones. This method effectively classifies the points that are in the border regions and identifies clusters with complex shapes. The authors of [28] proposed a method called DGDPC. In order to create clusters, each set of points with a decay phenomenon is first assigned to a separate cluster. Then, two clusters are combined using the connection points. In [29], to efficiently handle data of complex forms or multi-manifold structures, DPC is integrated with the minimum spanning tree. Whereas this algorithm has a significant computing resource need and is unable to recognize twisted, folded, or curved clusters.