Density Peaks Clustering Algorithm Research Articles

Uncertain data density peak clustering algorithm based on JS divergence

Aiming at the defects of traditional density-based uncertainty clustering algorithms, such as parameter sensitivity and poor clustering results for complex manifold uncertain data sets, a new uncertainty data density peak clustering algorithm based on JS divergence (UDPC-JS) is proposed. The algorithm first removes noise points by using the uncertain natural neighbor density factor defined by the uncertain natural neighbor. Secondly, the local density of uncertain data objects is calculated by combining the uncertain natural neighbor and JS divergence. The initial clustering center of the uncertain data set is found by combining the idea of representative points, and the distance between the initial clustering centers is defined based on JS divergence and graph. Then, the decision graph is constructed on the initial clustering center using the local density calculated based on the uncertain natural neighbor and JS divergence and the newly defined distance between the initial clustering centers based on JS divergence and graph, and the final clustering center is selected according to the decision graph. Finally, the unassigned uncertain data objects are assigned to the cluster where their initial clustering center is located. Experimental results show that the algorithm has better clustering effect and accuracy than the comparison algorithm, and has a greater advantage in processing complex manifold uncertain data sets.

Trust-aware and improved density peaks clustering algorithm for fast and secure models in wireless sensor networks

Many trust-based models for wireless sensor networks do not account for trust attacks, which are destructive phenomena that undermine the security and reliability of these models. Therefore, a trust-based fast security model fused with an improved density peaks clustering algorithm (TFSM-DPC) is proposed to quickly identify trust attacks in this paper. First, when calculating direct trust values, TFSM-DPC designs the adaptive penalty factors based on the state of received and sent packets and behaviors, and introduces the volatilization factors to reduce the effect of historical trust values. Second, TFSM-DPC improved density peaks clustering (DPC) algorithm to evaluate the trustworthiness of each recommendation value, thus filtering malicious recommendations before calculating the indirect trust values. Moreover, to filter two types of recommendations, the improved DPC algorithm incorporates artificial benchmark data along with trust values recommended by neighbors as input data. Finally, based on the relationship between direct trust and indirect trust, a secure formula for calculate the comprehensive trust is designed. Therefore, the proposed TFSM-DPC can improve the accuracy of trust evaluation and speed up the identification of malicious nodes. Simulation results show that TFSM-DPC can effectively identify on-off, bad-mouth and collusion attacks, and improve the speed of excluding malicious nodes from the network, compared to other trust-based algorithms.

Low-latency intelligent service combination caching strategy with density peak clustering algorithm in vehicle edge computing

In the dynamic field of Vehicle Edge Computing (VEC), the demand for intelligent vehicular systems to process vast amounts of data is escalating, driven by advancements in autonomous driving and real-time navigation technologies. Optimizing service latency and minimizing transmission costs are crucial for enhancing the performance of vehicular networks. Traditional service caching strategies, which largely rely on the popularity of individual services, often fail to account for the intricate interdependencies between services. The oversight can result in redundant data transfers and inefficient use of storage resources. In response, our paper introduces a novel approach to service combination caching within a heterogeneous computational framework comprising vehicles, edge servers, and the cloud. Our strategy focuses on minimizing user wait times and data transmission costs during task execution, while adhering to the caching budget constraints of service providers. Key contributions include the development of an Improved Density Peak Clustering (IDPC) algorithm to facilitate cooperative clustering among edge servers and the design of a Service Combination Caching Strategy (SCCS). The SCCS approach reduces caching costs by categorizing servers, forming efficient clusters, and strategically allocating storage. Simulation results demonstrate that the method outperforms existing strategies by significantly decreasing task execution delays and transmission costs, thereby greatly enhancing the quality of service in vehicular applications.

Semi-supervised learning for gas insulated switchgear partial discharge pattern recognition in the case of limited labeled data

Semi-supervised learning has better and more efficient performance than supervised algorithms in the case of limited labeled data. Existing methods for diagnosing partial discharge (PD) insulation defects in gas-insulated switchgear (GIS) equipment can only be effective if there is sufficient labeled data. However, in the actual working conditions of GIS equipment, insulation defect data is very scarce, where labeled data is more expensive to obtain and most of the data is unlabeled. In the case of limited PD labeled data, it is still a serious challenge to achieve higher classification accuracy of GIS PD pattern recognition. Therefore, we propose a semi-supervised self-training algorithm based on density peaks of local neighbor Information. Firstly, an improved density peak clustering algorithm based on local neighbor information is proposed, which no longer depends on the truncation distance, and considers the local information to better reflect the local density. Secondly, using local neighbor Information of labeled data, the criterion of confidence of unlabeled data is improved. Then, the PD unlabeled data with pseudo-labels are used to build a strong classifier for GIS PD pattern recognition. The experimental results show that the proposed algorithm has higher classification accuracy than other semi-supervised algorithms. When the proportion of labeled data is 10 %, the recognition accuracy can reach 65.98 %, which is the highest in the comparison algorithm. The proposed algorithm provides a feasible solution for GIS PD pattern recognition in the case of limited labeled data.

The Improvement of Density Peaks Clustering Algorithm and Its Application to Point Cloud Segmentation of LiDAR.

This work focuses on the improvement of the density peaks clustering (DPC) algorithm and its application to point cloud segmentation in LiDAR. The improvement of DPC focuses on avoiding the manual determination of the cut-off distance and the manual selection of cluster centers. And the clustering process of the improved DPC is automatic without manual intervention. The cut-off distance is avoided by forming a voxel structure and using the number of points in the voxel as the local density of the voxel. The automatic selection of cluster centers is realized by selecting the voxels whose gamma values are greater than the gamma value of the inflection point of the fitted γ curve as cluster centers. Finally, a new merging strategy is introduced to overcome the over-segmentation problem and obtain the final clustering result. To verify the effectiveness of the improved DPC, experiments on point cloud segmentation of LiDAR under different scenes were conducted. The basic DPC, K-means, and DBSCAN were introduced for comparison. The experimental results showed that the improved DPC is effective and its application to point cloud segmentation of LiDAR is successful. Compared with the basic DPC, K-means, the improved DPC has better clustering accuracy. And, compared with DBSCAN, the improved DPC has comparable or slightly better clustering accuracy without nontrivial parameters.

A CREAM model optimization method based on fatigue testing experiments and machine learning techniques for maritime transportation applications

Maritime transportation has a crucial position in world trade, but maritime transportation accidents still occur frequently. To assess human errors in maritime transportation more accurately by CREAM and thus improve the reliability of maritime transportation, the study is the first to obtain the quantitative effects of different anti-fatigue capability groups on task performance in the field of maritime transportation by combining fatigue test experiments with machine learning. First, a dataset reflecting the fatigue changes of participants in maritime transportation tasks is constructed by 91 subjects participating in fatigue test experiments. Second, anti-fatigue capability classifications were performed by machine learning algorithms based on Density Peak Clustering Algorithm and fitness. Again, the failure probability correction coefficients of different anti-fatigue capability classes are obtained based on the classification results. Finally, the proposed model optimization method is experimentally verified. The results show that the optimized model is more accurate for the estimation of human-caused errors in maritime transportation. The proposed model effectively improves the inaccuracy of the traditional CREAM due to the lack of attention to individual physiological differences in the reliability assessment.

Density peaks clustering based on density voting and neighborhood diffusion

Density Peaks Clustering (DPC) is a well-known clustering technique in the data mining field with fewer parameters as well as no iteration. However, when dealing with datasets containing multiple peaks, DPC may subjectively choose the wrong cluster centers through the decision graph. Additionally, DPC requires a considerable amount of time to estimate density and relative distance. Moreover, DPC is sensitive to the value of cut-off distance. To overcome these issues, a density peaks clustering algorithm based on density voting and neighborhood diffusion (DPC-DVND) is proposed. Firstly, the proposed algorithm utilizes the k nearest neighbors and KD-tree to enhance the efficiency of computing local density and relative distance. Secondly, this study selects the potential cluster centers by density voting and applies the number of votes instead of density to calculate the feasibility of each potential center becoming a cluster center, so that the centers of low-density clusters can be better distinguished. Finally, two neighborhood density diffusion rules are designed to propagate labels and form the core structure of clusters. Experiments on synthetic, real, and image datasets are performed to compare different methods. Results show that DPC-DVND outperforms other state-of-the-art algorithms in terms of effectiveness and efficiency.

Representative co-location pattern post-mining based on maximal row instances representation model

The mining result set of spatial prevalent co-location patterns(SPCPs) is often large and redundant, especially when the prevalence threshold is set to low or long SPCPs are present. Meanwhile, the distribution of SPCPs in continuous space and complex spatial relationships with each other of spatial data make the compression and reorganization of SPCPs a challenging problem. To solve this problem, in this paper, a representative co-location pattern mining framework based on the maximal row instance(MRI) representation model is proposed. First, the MRI representation model is proposed to effectively preserve the pattern distribution information of prevalent co-location patterns. To establish the MRI representation model, the basic algorithm and geometric algorithm are proposed in this paper. Two materialization methods based on the MRI representation model, 0-1 vector and key–value vector, are presented. Secondly, the similarity measure of SPCPs under the context of the MRI representation model is proposed, which calculates the similarity between any two co-location patterns without adding additional information such as domain background. Furthermore, the mining framework based on the k nearest neighbors density peak clustering algorithm is presented to extract representative co-location patterns. Finally, the efficiency and scalability of the proposed method are verified on the synthetic data and real data. Compared with the existing methods, the representative co-location patterns of the proposed method have well compression performances.

Rapid and non-destructive identification of Panax ginseng origins using hyperspectral imaging, visible light imaging, and X-ray imaging combined with multi-source data fusion strategies

The geographical origin of Panax ginseng significantly influences its nutritional value and chemical composition, which in turn affects its market price. Traditional methods for analyzing these differences are often time-consuming and require substantial quantities of reagents, rendering them inefficient. Therefore, hyperspectral imaging (HSI) in conjunction with X-ray technology were used for the swift and non-destructive traceability of Panax ginseng origin. Initially, outlier samples were effectively rejected by employing a combined isolated forest algorithm and density peak clustering (DPC) algorithm. Subsequently, random forest (RF) and support vector machine (SVM) classification models were constructed using hyperspectral spectral data. These models were further optimized through the application of 72 preprocessing methods and their combinations. Additionally, to enhance the model’s performance, four variable screening algorithms were employed: SelectKBest, genetic algorithm (GA), least absolute shrinkage and selection operator (LASSO), and permutation feature importance (PFI). The optimized model, utilizing second derivative, auto scaling, permutation feature importance, and support vector machine (2nd Der-AS-PFI-SVM), achieved a prediction accuracy of 93.4 %, a Kappa value of 0.876, a Brier score of 0.030, an F1 score of 0.932, and an AUC of 0.994 on an independent prediction set. Moreover, the image data (including color information and texture information) extracted from color and X-ray images were used to construct classification models and evaluate their performance. Among them, the SVM model constructed using texture information from X -ray images performed the best, and it achieved a prediction accuracy of 63.0 % on the validation set, with a Brier score of 0.181, an F1 score of 0.518, and an AUC of 0.553. By implementing mid-level fusion and high-level data fusion based on the Stacking strategy, it was found that the model employing a high-level fusion of hyperspectral spectral information and X-ray images texture information significantly outperformed the model using only hyperspectral spectral information. This advanced model attained a prediction accuracy of 95.2 %, a Kappa value of 0.912, a Brier score of 0.027, an F1 score of 0.952, and an AUC of 0.997 on the independent prediction set. In summary, this study not only provides a novel technical path for fast and non-destructive traceability of Panax ginseng origin, but also demonstrates the great potential of the combined application of HSI and X-ray technology in the field of traceability of both medicinal and food products.

Flexible density peak clustering for real-world data

In density based clustering, the density peak algorithm has attracted much attention due to its effectiveness and simplicity, and a vast amount of clustering approaches have been proposed based on this algorithm. Some of these works require manual selection of cluster centers with a decision graph, where human involvement leads to uncertainty in clustering results. In order to avoid human involvement, some other algorithms depend on user-specified number of clusters to determine cluster centers automatically. However, it is well known that accurate estimation of number of clusters is a long-standing difficulty in data clustering. In this paper we present a sequential density peak clustering algorithm to extract clusters one by one, thereby determining the number of clusters automatically and avoiding manual selection of cluster centers in the meanwhile. Starting from a density peak, our algorithm generates an initial cluster surrounding the density peak in the first step, and then obtains the final cluster by expanding the initial cluster based on the relative density relationship among neighboring data points. With a peeling-off strategy, we obtain all the clusters sequentially. Our algorithm works well with clusters of Gaussian distribution and is therefore potential for clustering of real-world data. Experiments with a large number of synthetic and real datasets and comparisons with existing algorithms demonstrate the effectiveness of the proposed algorithm.

A Delayed Spiking Neural Membrane System for Adaptive Nearest Neighbor-Based Density Peak Clustering.

Although the density peak clustering (DPC) algorithm can effectively distribute samples and quickly identify noise points, it lacks adaptability and cannot consider the local data structure. In addition, clustering algorithms generally suffer from high time complexity. Prior research suggests that clustering algorithms grounded in P systems can mitigate time complexity concerns. Within the realm of membrane systems (P systems), spiking neural P systems (SN P systems), inspired by biological nervous systems, are third-generation neural networks that possess intricate structures and offer substantial parallelism advantages. Thus, this study first improved the DPC by introducing the maximum nearest neighbor distance and K-nearest neighbors (KNN). Moreover, a method based on delayed spiking neural P systems (DSN P systems) was proposed to improve the performance of the algorithm. Subsequently, the DSNP-ANDPC algorithm was proposed. The effectiveness of DSNP-ANDPC was evaluated through comprehensive evaluations across four synthetic datasets and 10 real-world datasets. The proposed method outperformed the other comparison methods in most cases.

Random walk with Brownian motion: Theory and application

While physical models are classical, random walks are statistical models. Historically, Karl Pearson was the first to propose the random walk. This study will focus on the essential of the random walk, and additionally, this essay will offer some application guidelines and explain when the random walk is applicable. The random walk means that the performance of an event is based on the past, as people cannot predict the future development steps and directions. The random walk, which dates to the 16th century, is a significant area of probability with many applications. The random walk and Brownian motion are the main topics of this paper. The four applications of random walks that are highlighted in this paper are the following. They include the multi-particle random walk immune algorithm, the density peak clustering algorithm, a review of random walk-based community discovery techniques, and interactive image segmentation that combines random walk and random forest. This work paves the way to unravel the importance of random walk.

ResGRU: A deep learning approach for settlement prediction in CFRD based on the spatiotemporal feature fusion method

Settlement is one of the most critical safety evaluation indicators for concrete face rock-fill dams. Deformation measurement data exhibit spatiotemporal correlation due to similar environmental factors and material properties. However, the current research has focused mainly on single-point prediction models, which ignore the relationship between settlement values and various parts of dams. To address these issues, the spatiotemporal correlation of settlements across multiple measurement points is considered and a deep learning approach based on spatiotemporal feature fusion is proposed. First, a density peak clustering algorithm categorizes monitoring points characterized by analogous deformation patterns. Then, a deep learning model combining residual neural networks (ResNet) and gated recurrent unit neural networks (GRU), i.e. ResGRU, is established. In ResGRU, ResNet is used to map the input data’s features to more complex high-dimensional patterns through convolution and obtain deep nonlinear spatial features. GRU is used to capture the temporal dependencies of data to obtain temporal features. Finally, the spatiotemporal features are fused through fully connected layers to predict settlements. The model is validated and compared with deep learning models using monitoring data collected from an actual concrete face rock-fill dam. The proposed approach demonstrates superior performance with an average RMSE and MAE of 0.352 and 0.255, respectively, and an average R2 of 0.988. The results show that the ResGRU model generally outperforms the other models. This approach provides an efficient and convenient approach for the settlement analysis of concrete face rock-fill dams.

An improved density peaks clustering algorithm using similarity assignment strategy with K-nearest neighbors

An improved density peaks clustering algorithm using similarity assignment strategy with K-nearest neighbors

Density peaks clustering based on superior nodes and fuzzy correlation

The Density Peaks Clustering (DPC) algorithm is simple and efficient, but still has a few limitations. For example, DPC needs manual selection of clustering centers and may miss the correct cluster when searching for denser nearest neighbors, which may cause incorrect allocation of data points. To address these limitations, this work proposes a novel density peaks clustering algorithm based on superior nodes and fuzzy correlation (DPC-SNFC). Reverse nearest neighbors are used first to find the nearest point with a higher density as the superior node. Fuzzy correlation is then applied to construct connectivity subgraphs without using clustering centers. The connectivity subgraphs can identify the different clusters. Extensive experiments are conducted using 12 synthetic datasets and 10 real datasets and using 6 state-of-the-art baseline algorithms. The experimental results show that the proposed DPC-SNFC algorithm outperforms the baseline algorithms, which validates its efficiency.

ANN-DPC: Density peak clustering by finding the adaptive nearest neighbors

DPC(clustering by fast search and find of density peaks) is an efficient clustering algorithm. However, DPC and its variations usually cannot detect the appropriate cluster centers for a dataset containing sparse and dense clusters simultaneously, resulting in the unique clustering within a dataset cannot being found. To remedy these limitations, we propose an Adaptive Nearest Neighbor Density Peak Clustering algorithm, referred to as ANN-DPC. It introduces the adaptive nearest neighbors for a point, so as to define the accurate local density of the point. Moreover, it partitions points into super-score, core, linked and slave points, and proposes techniques to detect appropriate cluster centers through introducing super-core point with higher local density to absorb the other super-core points sharing adaptive nearest neighbors with it and the dependency vector for finding next cluster center. Furthermore, novel assignment strategies are proposed by leveraging the adaptive nearest neighbors combing with breadth first search and fuzzy weighted adaptive nearest neighbors, so as to assign non-center points to the most appropriate clusters. Extensive experiments on real-world and synthetic datasets demonstrate the superiority of the proposed ANN-DPC algorithm over the counterparts in precisely detecting the cluster centers and the unique clustering within a dataset.

An Improved Density Peaks Clustering Algorithm Based On Density Ratio

Abstract Density peaks clustering (DPC) is a relatively new density clustering algorithm. It is based on the idea that cluster centers always have relatively high local densities and are relatively far from the points with higher densities. With the aforementioned idea, a decision graph can be drawn, and cluster centers will be chosen easily with the aid of the decision graph. However, the algorithm has its own weaknesses. Because the algorithm calculates local density and allocates points based on the distances between certain points, the algorithm has difficulty in classifying points into proper groups with varying densities or nested structures. This paper proposes an improved density peaks clustering algorithm called Dratio-DPC to overcome this weakness. First, Dratio-DPC adjusts the original local density with a coefficient calculated with the density ratio. Second, Dratio-DPC takes density similarity into consideration to calculate the distances between one point and other points with higher local densities. We design and perform experiments on different benchmark datasets and compare the clustering results of Dratio-DPC, traditional clustering algorithms and three improved DPC algorithms. Comparison results show that Dratio-DPC is effective and applicable to a wider range of scenarios.

A novel tree structure-based multi-prototype clustering algorithm

Density-peak clustering (DPC) is a novel clustering algorithm that has received much attention from researchers. Its principle is to identify the cluster center of the data by density and distance. However, the density-peak clustering algorithm still suffers from poor clustering results due to irrational choices of cluster centers and misallocating samples. To tackle these problems, a novel tree structure-based multi-prototype clustering algorithm (MPCTS) is proposed in this paper. Firstly, a division-merger method is adopted to divide the dataset into multiple peak clusters based on multi-prototype clustering, which could minimize the impact of high-density sample points on the subsequent sample point assignments. Secondly, the KNN nearest-neighbor search strategy is improved to quickly obtain the sub-cluster center in each peak cluster to overcome the defect of irrational selection of initial cluster centers. Afterward, a tree-based decay strategy is designed to evaluate the correlation between sample points, and a tree-based association strategy is designed to achieve the merging of multiple peak clusters to tackle the sample points misallocation problem. Finally, the extensive experiments on clustering task are conducted over several benchmark synthetic and real datasets to verify the efficiency and superiority of the proposed MPCTS.

User profiling for Chinese super-new generation wine consumers based on improved density peak clustering algorithm

PurposeFor a better understanding of the preferences and differences of young consumers in emerging wine markets, this study aims to propose a clustering method to segment the super-new generation wine consumers based on their sensitivity to wine brand, origin and price and then conduct user profiles for segmented consumer groups from the perspectives of demographic attributes, eating habits and wine sensory attribute preferences.Design/methodology/approachWe first proposed a consumer clustering perspective based on their sensitivity to wine brand, origin and price and then conducted an adaptive density peak and label propagation layer-by-layer (ADPLP) clustering algorithm to segment consumers, which improved the issues of wrong centers' selection and inaccurate classification of remaining sample points for traditional DPC (DPeak clustering algorithm). Then, we built a consumer profile system from the perspectives of demographic attributes, eating habits and wine sensory attribute preferences for segmented consumer groups.FindingsIn this study, 10 typical public datasets and 6 basic test algorithms are used to evaluate the proposed method, and the results showed that the ADPLP algorithm was optimal or suboptimal on 10 datasets with accuracy above 0.78. The average improvement in accuracy over the base DPC algorithm is 0.184. As an outcome of the wine consumer profiles, sensitive consumers prefer wines with medium prices of 100–400 CNY and more personalized brands and origins, while casual consumers are fond of popular brands, popular origins and low prices within 50 CNY. The wine sensory attributes preferred by super-new generation consumers are red, semi-dry, semi-sweet, still, fresh tasting, fruity, floral and low acid.Practical implicationsYoung Chinese consumers are the main driver of wine consumption in the future. This paper provides a tool for decision-makers and marketers to identify the preferences of young consumers quickly which is meaningful and helpful for wine marketing.Originality/valueIn this study, the ADPLP algorithm was introduced for the first time. Subsequently, the user profile label system was constructed for segmented consumers to highlight their characteristics and demand partiality from three aspects: demographic characteristics, consumers' eating habits and consumers' preferences for wine attributes. Moreover, the ADPLP algorithm can be considered for user profiles on other alcoholic products.

Fuzzy Regional Co-Location Pattern Mining Based on Efficient Density Peak Clustering and Maximal Fuzzy Grid Cliques

Due to the heterogeneity of data distribution in real life and the spatial autocorrelation among spatial instances, traditional spatial co-location pattern mining methods tend to ignore valuable information specific to local regions. To address the limitation, regional co-location pattern mining has been proposed to find patterns that may be hidden within local regions. In this paper, a fuzzy regional co-location pattern mining framework based on efficient density peak clustering and maximal fuzzy grid cliques is presented. By incorporating a grid-splitting method and fuzzy theory, an efficient density peak clustering algorithm is proposed to divide the global area into distinct local regions. Furthermore, we propose a method to materialize the neighbor relationships between instances based on the maximal fuzzy grid cliques, and parallelize the clustering process to improve the algorithm efficiency. Experimental results show that the proposed algorithm can not only reduce the time consumption by about 40%, but also mine meaningful patterns with tighter instance distributions.