Density Peak Clustering Algorithm Based on Natural Neighbor and Diffusion Assignment

Clustering is one of the most important topics in data mining and machine learning. The density peaks clustering (DPC) algorithm is a well-known density-based clustering method that can efficiently and effectively deal with non-spherical clusters. However, the computational methods of the local density and the distance measure are simple and easily ignore the correlation and the similarity between samples, and the manual setting of parameters has a great influence on the clustering results; therefore, the clustering performance of DPC is poor on the high-dimensional datasets. To address these issues, this paper presents an adaptive DPC algorithm with Fisher linear discriminant for the clustering of complex datasets, called ADPC-FLD. First, the kernel density estimation function is introduced to calculate the local density of the sample points. Pearson correlation coefficient between samples as weight is employed to construct a weighted Euclidean distance function to measure the distance between samples. This considers both the spatial structure and the correlation of the samples. Then, a novel density estimation entropy is proposed, and based on the minimization of density estimation entropy, the density estimation parameters are adaptively selected according to the distribution characteristics of the data, which can efficiently eliminate the influence of manual setting. Third, an adaptive strategy of cluster center selection is designed to avoid the error caused by the noise data as the cluster centers and the uncertainty of manually selecting the cluster centers. Finally, Fisher linear discriminant algorithm is used to eliminate the irrelevant information and reduce the dimensionality of high-dimensional data, following on which an adaptive DPC method is implemented on six synthetic datasets, thirteen UCI datasets and seven gene expression datasets for comparing with other related algorithms. The experimental results on 26 datasets show that the proposed algorithm significantly outperforms several outstanding clustering approaches in terms of clustering accuracy and efficiency.

DPC-FSC: An approach of fuzzy semantic cells to density peaks clustering

An improved density peaks clustering algorithm based on natural neighbor with a merging strategy

Density peak clustering with attribute-level similarity and two-stage allocation

Fluid evaluation plays a vital role in reserve calculation and perforation location selection in shale oil exploration. Manual classification with fixed cutoffs and the blind source separation (BSS) method are the main approaches used for fluid typing in NMR T1–T2 maps. To overcome the subjectivity of the manual method and the dependence of BSS on a large dataset, an approach that integrates a clustering method, density peak clustering (DPC), and a spectral fitting technique, the Gaussian mixture model (GMM), is introduced in this paper to classify fluid components and evaluate oil saturation from T1–T2 maps. To select the number of fluid types automatically, traditional DPC is improved by defining a metric rnew based on local outlier degree, local density, relative distance, and threshold r*. The clustering accuracy of the r*−DPC method is 83.64%, which is much higher than the 52.73% accuracy of traditional DPC, and the r*–DPC method achieves better performance in fluid partitioning than other commonly used clustering algorithms. With the fluid centers information provided by r*–DPC, the GMM method is implemented to fit and extract the T1–T2 signatures of multiple fluids from T1–T2 maps. The DPC–GMM method was applied to core T1–T2 measurements and T1–T2 logging datasets, and the experimental results reveal that the average relative error of oil saturation is between 17.10% and 21.63%, which is nearly 15% lower than that of the BSS method. Furthermore, the DPC–GMM method requires less time and is easier to implement, which can make it an efficient and practical approach to fluid evaluation.

Density peaks clustering based on superior nodes and fuzzy correlation

The density peaks clustering (DPC) algorithm can identify clusters with various shapes and densities in the underlying dataset. However, the DPC algorithm cannot exactly find the true quantity of clustering centers when computing the local density, and it is difficult to handle non-convex datasets. Moreover, the DPC algorithm is difficult to identify boundary points and outliers without a reasonable allocation strategy when dealing with low-density points. To solve these limitations, a novel deviation density peaks clustering (DeDPC) algorithm is proposed. First, the local deviation of the spatial distance of datasets with different structures is utilised to replace the local density to generate a more reasonable clustering center decision graph. Second, a threshold is defined to further divide and process low-density points. Finally, outliers in low-density points can be accurately found to accurately cluster the dataset. To evaluate the performance of the DeDPC algorithm, experiments are conducted on synthetic and real-world datasets and the DeDPC is compared with other clustering methods. The DeDPC is also applied to medical image segmentation to further demonstrate its capability for medical image processing. The simulation results show that the DeDPC method has good validity and utility for both non-convex datasets and medical image segmentation.

A novel density peaks clustering algorithm based on Hopkins statistic

Density peak clustering based on relative density relationship

Density Peaks Clustering (DPC) is a density-based clustering algorithm that has the advantage of not requiring clustering parameters and detecting non-spherical clusters. The density peaks algorithm obtains the actual cluster center by inputting the cutoff distance and manually selecting the cluster center. Thus, the clustering center point is not selected on the basis of considering the whole data set. This paper proposes a method called G-KNN-DPC to calculate the cutoff distance based on the Gini coefficient and K-nearest neighbor. G-KNN-DPC first finds the optimal cutoff distance with Gini coefficient, and then the center point with the K-nearest neighbor. The automatic clustering center method can not only avoid the error that a cluster detects two center points but also effectively solve the traditional DPC algorithm defect that cannot handle complex data sets. Compared with DPC, Fuzzy C-Means, K-means, KDPC and DBSCAN, the proposed algorithm creates better clusters on different data sets.

Clustering by fast search and find of density peaks (CFSFDP) is a new clustering method that can find the cluster centers, quickly. The core idea of CFSFDP is the depiction on cluster centers, which based on two assumptions: the cluster center is surrounded by points whose density does not exceed it, and is relatively more distant from other denser points. Based on these two assumptions, CFSFDP can select the cluster centers manually depending on decision graph. But, in the complex and diverse clustering tasks, selecting the cluster centers through the decision graph still has a great limitation. To solve this problem, a Fuzzy CFSFDP clustering algorithm has been proposed, which can select cluster centers through fuzzy rules, automatically. However, the performance of Fuzzy CFSFDP is usually affected by the cutoff distance d <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</inf> . Moreover, the clustering of data with complex manifold structure distribution is not effective, resulting in incorrect clustering results. In this paper, we propose to optimize the Fuzzy CFSFDP algorithm using manifold distance and standard deviation based cutoff distance. We compared the Optimized Fuzzy CFSFDP with original Fuzzy CFSFDP on two synthetic datasets, clustering results verified the effectiveness of our method.

Aiming at the problems that the initial cluster centers are randomly selected and the number of clusters is manually determined in traditional clustering algorithm, which results in unstable clustering results, we propose an improved clustering algorithm based on density peak and nearest neighbors. Firstly, an improved density peak clustering method is proposed to optimize the cutoff distance and local density of data points. It avoids that random selection of initial cluster centers is easy to fall into the local optimal solution. Furthermore, a K-value selection method is presented to choose the optimal number of clusters, which is determined by the sum of the squared errors within the clusters. Finally, we employ the idea of the K-nearest neighbors to carry out the assignment for outliers. Experiments on the UCI real data sets indicate that our proposed algorithm can achieve better clustering results compared with several known algorithms.

In this paper, we propose a new path planning algorithm based on the probabilistic roadmaps method (PRM), in order to effectively solve the autonomous path planning of mobile robots in complex environments with multiple narrow channels. The improved PRM algorithm mainly improves the density and distribution of sampling points in the narrow channel, through a combination of the learning process of the PRM algorithm and the APF algorithm. We also shortened the required time and path length by optimizing the query process. The first key technology to improve the PRM algorithm involves optimizing the number and distribution of free points and collision-free lines in the free workspace. To ensure full visibility of the narrow channel, we extend the obstacles through the diagonal distance of the mobile robot while ignoring the safety distance. Considering the safety distance during movement, we re-classify the all sampling points obtained by the quasi-random sampling principle into three categories: free points, obstacle points, and adjacent points. Next, we transform obstacle points into the free points of the narrow channel by combining the APF algorithm and the characteristics of the narrow channel, increasing the density of sampling points in the narrow space. Then, we include potential energy judgment into the construction process of collision-free lines shortening the required time and reduce collisions with obstacles. Optimizing the query process of the PRM algorithm is the second key technology. To reduce the required time in the query process, we adapt the bidirectional A* algorithm to query these local paths and obtain an effective path to the target point. We also combine the path pruning technology with the potential energy function to obtain a short path without collisions. Finally, the experimental results demonstrate that the new PRM path planning technology can improve the density of free points in narrow spaces and achieve an optimized, collision-free path in complex environments with multiple narrow channels.

Density-peak clustering has garnered significant attention due to its ability to identify non-convex cluster shapes without requiring a predefined number of clusters. However, traditional DPC still exhibits shortcomings in cluster center selection and density estimation, often resulting in unstable clustering performance under complex data distributions. This paper proposes an enhanced algorithm—Region-based Density Peak Clustering (RDPC). The method first constructs a more robust candidate center set through region-based sub-cluster formation and regional peak selection. Subsequently, it introduces a multi-factor decision metric combining density, relative distance, and affinity to screen the final clustering centers. Extensive experiments on synthetic and real-world datasets validate RDPC's effectiveness. Results demonstrate superior performance over traditional methods in terms of ARI and AMI metrics, exhibiting notably higher robustness and stability in complex distributions and noisy data.

Density Peak Clustering (DPC) is a widely used clustering method that automatically identifies cluster centers based on local density and distance metrics. However, DPC tends to converge to local optimal solutions when processing datasets with uneven density distributions. This paper proposes DPC-DIST, an improved algorithm that addresses this limitation through a geometric distribution coefficient δ . By incorporating spatial distribution characteristics into density calculations, DPC-DIST effectively prevents convergence to suboptimal solutions. Comprehensive experiments on six benchmark datasets (Wine, Iris, Seeds, Blobs, R15, R3) demonstrate that DPC-DIST consistently outperforms the original DPC, K-means++, and spectral clustering algorithms. Significant improvements were observed in high-dimensional data processing, with 9.3 % increase in Silhouette Coefficient and 47.7 % improvement in Calinski-Harabasz Score on the Wine dataset. The algorithm shows particular advantages in facility location optimization and logistics planning applications, demonstrating its practical value for real-world scenarios.