A Novel Density Peaks Clustering Halo Node Assignment Method Based on K-Nearest Neighbor Theory

Wang, Limin; Wei, Zhou; Wang, Honghuan; Parmar, Milan; Han, Xuming

doi:10.1109/access.2019.2957242

Cited by 11 publications

(10 citation statements)

References 34 publications

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“…Even if the final clustering effect is good, the adjustment cost is high. In terms of algorithm combination, the proposed KNN-HDPC algorithm [23] makes the combination of KNN and DPC possible. In addition, the density peak clustering based on improved mutual K-Nearest Neighbor graph [24] solves the problem of poor clustering effect when different density regions are adjacent in DPC.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Density Peak Clustering Algorithm Based on K-Nearest Neighbor

Zhai

Wang

et al. 2022

Security and Communication Networks

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The clustering results of the density peak clustering algorithm (DPC) are greatly affected by the parameter d c , and the clustering center needs to be selected manually. To solve these problems, this paper proposes a low parameter sensitivity dynamic density peak clustering algorithm based on K-Nearest Neighbor (DDPC), and the clustering label is allocated adaptively by analyzing the distribution of K-Nearest Neighbors around each data. It reduces the parameter sensitivity and eliminates selecting the clustering centers manually from the decision graph. Through the experimental analysis and comparison of the artificial dataset and UCI dataset, the results show that the comprehensive clustering effect of DDPC is better than DPC, DBSCAN, DBC, and other algorithms.

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Section: Introductionmentioning

confidence: 99%

A Dynamic Density Peak Clustering Algorithm Based on K-Nearest Neighbor

Zhai

Wang

et al. 2022

Security and Communication Networks

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“…DPC algorithm is not sensitive to the recognition of halo nodes, and the edge of the halo node has a low local density and outliers are often distributed in it. In order to accurately grasp the outliers and cluster points, Wang et al [35] proposed a density peak clustering corona node assignment algorithm based on K-nearest neighbor theory (KNN-HDPC) by taking advantage of the advantages of KNN algorithm with high accuracy, insensitivity to outliers, and no input hypothesis data. Combined with KNN algorithm, this algorithm strengthens the processing ability of HaloDPC algorithm [36] for halo nodes, and improves the original classi cation and clustering process.…”

Section: Other Improved Dpc Algorithmsmentioning

confidence: 99%

An overview on density peaks clustering

Wei

Peng

Huang

2023

Preprint

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Density Peaks Clustering (DPC) algorithm is a new algorithm based on density clustering analysis, which can quickly obtain the cluster centers by drawing the decision diagram by using the calculation of local density and relative distance. Without prior knowledge and iteration, the parameters and structure are simple and easy to implement. Since it was proposed in 2014, it has attracted a large number of researchers to explore experiments and improve applications in recent years. In this paper, we first analyze the theory of DPC and its performance advantages and disadvantages. Secondly, it summarizes the improvement of DPC in recent years, analyzes the improvement effect, and shows it with experimental data. Finally, the related application research of DPC in different fields is introduced. At the same time, we summarize and prospect the improvement and development of DPC.

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“…en, DPC selects the data points with both ρ i and δ i as the cluster centers and assigns the remaining data points to the nearest class. DPC is a simple and efficient algorithm, and a series of works have been carried out [16][17][18][19][20][21][22]. However, DPC requires a huge computational overhead.…”

Section: Density Peaks Clustering Density Peaks Clustering (Dpc) Is Proposed Inmentioning

confidence: 99%

Density Peaks Clustering Based on Feature Reduction and Quasi-Monte Carlo

Wei

Han

et al. 2022

Scientific Programming

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Density peaks clustering (DPC) is a well-known density-based clustering algorithm that can deal with nonspherical clusters well. However, DPC has high computational complexity and space complexity in calculating local density ρ and distance δ , which makes it suitable only for small-scale data sets. In addition, for clustering high-dimensional data, the performance of DPC still needs to be improved. High-dimensional data not only make the data distribution more complex but also lead to more computational overheads. To address the above issues, we propose an improved density peaks clustering algorithm, which combines feature reduction and data sampling strategy. Specifically, features of the high-dimensional data are automatically extracted by principal component analysis (PCA), auto-encoder (AE), and t-distributed stochastic neighbor embedding (t-SNE). Next, in order to reduce the computational overhead, we propose a novel data sampling method for the low-dimensional feature data. Firstly, the data distribution in the low-dimensional feature space is estimated by the Quasi-Monte Carlo (QMC) sequence with low-discrepancy characteristics. Then, the representative QMC points are selected according to their cell densities. Next, the selected QMC points are used to calculate ρ and δ instead of the original data points. In general, the number of the selected QMC points is much smaller than that of the initial data set. Finally, a two-stage classification strategy based on the QMC points clustering results is proposed to classify the original data set. Compared with current works, our proposed algorithm can reduce the computational complexity from O n 2 to O N n , where N denotes the number of selected QMC points and n is the size of original data set, typically N ≪ n . Experimental results demonstrate that the proposed algorithm can effectively reduce the computational overhead and improve the model performance.

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A Novel Density Peaks Clustering Halo Node Assignment Method Based on K-Nearest Neighbor Theory

Cited by 11 publications

References 34 publications

A Dynamic Density Peak Clustering Algorithm Based on K-Nearest Neighbor

A Dynamic Density Peak Clustering Algorithm Based on K-Nearest Neighbor

An overview on density peaks clustering

Density Peaks Clustering Based on Feature Reduction and Quasi-Monte Carlo

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