SMC-PHD Target State Extraction Based on CFSFDP Clustering Algorithm

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.

Density Peaks Clustering (DPC) algorithm identifies cluster centers based on local density and relative distance, disregarding the effect of the sample's environment on its density. This makes it challenging to identify cluster centers in low-density regions. Moreover, the single-step allocation strategy of DPC algorithm lacks fault tolerance. If a sample point is incorrectly allocated, it can trigger a cascade of subsequent allocation error. To solve the aforementioned issue, a density peak clustering algorithm based on natural neighbors and diffusion allocation (NNDA-DPC) is proposed. Firstly, the concept of natural neighbors is introduced, and the local density of sample points is redefined by considering the density of sample points and the environment in which the sample points are located. This is done to reduce the influence of the sparsity of class clusters on the selection of class cluster centers. The clustering centers are automatically determined by computing the decision value. Secondly, the natural neighbor points of the selected clustering centers are assigned to the corresponding clustering centers to form the initial microclusters. Finally, the initial microclusters are formed based on the remaining sample points and the initial clusters. The final clustering result is obtained by assigning the sample points based on the correlation between the remaining sample points and the initial clusters, which helps prevent the cascading effect of assignment errors. We compared the NNDA-DPC algorithm with DPC and its enhanced versions on artificial and UCI datasets. The experimental results demonstrate that the NNDA-DPC algorithm can efficiently cluster the datasets.

Density peaks clustering based on superior nodes and fuzzy correlation

Density peaks clustering algorithm is a simple but effective clustering method, which requires fewer parameters and iteration, and can determine the number of clusters. But this algorithm has high complexity of time and space which makes it is unsuitable to cluster large-scale data. So that this paper proposes a density peaks clustering algorithm based on the divide-and-conquer strategy. Firstly, divides the large-scale data into a series of data blocks consistent with the original data distribution, then performs density peaks clustering on a randomly selected block to get the clustering center of the data block. Since the data block has the same distribution with the original large-scale data, the clustering center can be used as the clustering center of the original data. Finally, allocates the remaining data blocks to the corresponding clustering center to obtain the final clustering result. Experimental results on real and synthetic datasets verify the effectiveness of the proposed algorithm.

As a relatively novel density-based clustering algorithm, Density peak clustering (DPC) has been widely studied in recent years. DPC sorts all points in descending order of local density and finds neighbors for each point in turn to assign all points to the appropriate clusters. The algorithm is simple and effective but has some limitations in applicable scenarios. If the density difference between clusters is large or the data distribution is in a nested structure, the clustering effect of this algorithm is poor. This study incorporates the idea of connectivity into the original algorithm and proposes an improved density peak clustering algorithm ConDPC. ConDPC modifies the strategy of obtaining clustering center points and assigning neighbors and improves the clustering accuracy of the original density peak clustering algorithm. In this study, clustering comparison experiments were conducted on synthetic data sets and real-world data sets. The compared algorithms include original DPC, DBSCAN, K-means and two improved algorithms over DPC. The comparison results prove the effectiveness of ConDPC.

Density decay graph-based density peak clustering

Modified FDP cluster algorithm and its application in protein conformation clustering analysis

As one of the most important techniques in data mining, clustering has always been highly concerned. Most clustering algorithms have encountered challenges, such as the difficulty of cluster centers selection, the artificial determination of the number of clusters K, low accuracy of clustering, and uneven clustering efficiency of different data sets. Considering the difficulty of cluster centers chosen, a new algorithm of fast selecting the initial cluster centers is proposed in this paper. Generally, cluster centers are those data points with higher density, smaller radius threshold and far away from each other, this method uses MNN (M nearest neighbors), density and distance to determine the initial cluster centers. First, the neighborhood radius r of each point is measured by MNN based on distance, and the average value of all r is marked as r̅; second, the densities ρ of each point in the region within r̅ are calculated; and then, factor f is defined to describe the probability that points become cluster centers, based on which, the initial cluster centers are determined by the candidates with bigger f . In the end, the method proposed in this paper is tested by using 12 groups of typical benchmark data sets and applied in the stellar spectral data of LAMOST survey. The experiment results compared with the other six algorithms indicate that the initial cluster centers obtained by this method are of higher quality than that of the six algorithms. Meanwhile, the initial cluster centers of spectral data are of good agreement with the actual stellar classifications.

Adaptive density peak clustering based on K-nearest neighbors with aggregating strategy

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.

In software‐defined wide area networks (SD‐WANs), finding an optimal number of controllers and their placement is termed as the controller placement problem. The existing solutions based on multi‐objective optimisation techniques incur high computation time. Heuristic algorithms based methods quickly converge to local optimum solutions and clustering‐based approaches demand that the number of controllers required to be predefined. In SD‐WANs, computationally simple solutions for controller placement can be developed using non‐parametric clustering algorithms. The proposed modified density peak (modified‐DP) clustering algorithm decides the multi‐controller placement for the selected networks from the Internet Topology Zoo. The computed performance metrics are compared with hierarchical k‐means, modified affinity propagation (modified‐AP), and basic DP (basic‐DP) clustering algorithms. The criteria importance through inter‐criteria correlation method calculates weights of each performance metric, and a normalised weighted additive utility function computes the utility value for the controller placement using each algorithm. The modified‐DP computes the minimum utility value and optimal placement of SDN controllers compared to hierarchical k‐means, modified‐AP, and basic‐DP. The Cbench tool calculates the maximum throughput for the controller placement in each network topology. The calculated fairness in the utilisation of the controllers justifies the optimal controller placement using modified‐DP.

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

Water flooding flowing area identification for oil reservoirs based on the method of streamline clustering artificial intelligence

Density peak clustering based on relative density relationship

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.