# Density Peaks Clustering Algorithm Based on a Divergence Distance and Tissue--Like P System
## 1 Introduction  
As an unsupervised machine learning method, clustering analysis separates the datasets into several groups or categories, so points in the same group are similar to each other, while points in different groups are dissimilar [1]. Currently, multiple fields have benefited from clustering analysis, such as data mining [2], image segmentation [3], pattern recognition [4], machine learning [5] and others. There have been thousands of clustering algorithms created for different applications and data characteristics, which can be primarily categorized into partition-based methods [6], hierarchy-based methods [7], grid-based methods [8], model-based methods [9] and density-based methods [10]. During the development of clustering methods, many classic algorithms have emerged. K-means [11] is a centroid-based method that is representative of partition-based algorithms. In K-means algorithms, the closest objects are clustered and classified by their distance from k points in the space. Cluster center values are successively updated to achieve the best clustering result from the iterative process. The algorithm has been extensively employed in several big data processing sectors because of its quick operation speeds and straightforward execution processes. Data objects are hierarchically decomposed using hierarchical methods. Hierarchical clustering can identify and process an unknown number of clusters. Hierarchical methods can be cataloged as cohesive or divisive methods. BIRCH [12] and Chameleon algorithms are two typical hierarchical clustering algorithms. The flaw with the hierarchical approach is that once a step (merge or split) is complete, it cannot be undone. Multi-resolution grids are used for clustering, such as STING [13], and there is no relationship between the amount of data objects processed and the processing time. All that matters is the size of each dimension. For model-based methods, a model is assumed for each cluster, and then a dataset is found that meets this model. The densitydistribution of data points in space or another factor might be the basis for such a model. Model-based techniques typically relate to techniques based on neural network models and probability models. Gaussian Mixture Models [14] (GMM, Gaussian Mixture Models) serve as a representation for the former, and Self-Organizing Mapping Networks [15] (SOM, Self-Organized Maps) serve as a representation for the latter. A representative density-based clustering algorithm is called DBSCAN [16]. When compared to partitioned and hierarchical clustering algorithms, it can separate areas with adequate density into clusters, locate clusters of any shape in noisy spatial datasets, and define a cluster as the largest collection of densely linked points. However, DBSCAN has some shortcomings; the improper selection of parameters and MinPts will affect the overall clustering effect, and it cannot handle datasets with large density differences. To address the disadvantage of clustering analysis utilizing a set of global parameters, the OPTICS [17] algorithm is proposed. OPTICS does not intentionally generate dataset clusters, but outputs cluster ordering. HDBSCAN [18] is a clustering algorithm developed by [PERSON], [PERSON] and [PERSON]. It extends DBSCAN by converting DBSCAN into a hierarchical clustering algorithm, and then uses the technique of extracting planar clustering based on clustering stability. The biggest difference between HDBSCAN and traditional DBSCAN is that HDBSCAN can handle clustering problems with different densities. However, the processing effect of boundary points is not ideal. [PERSON] and [PERSON] recently introduced the Density Peaks Clustering method (DPC) [19] in Science, a novel density-based algorithm. DPC is based on the supposition that (1) the clustering center has a higher density than the neighboring points in its immediate vicinity; (2) the clustering center point is comparatively far from the places with greater densities.