Effective Density Peaks Clustering Algorithm Based on the Layered K-Nearest Neighbors and Subcluster Merging

Ren, Chunhua; Sun, Linfu; Yu, Yang; Wu, Chase Q.

doi:10.1109/access.2020.3006069

Cited by 20 publications

(7 citation statements)

References 30 publications

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“…Each color stands for one cluster. We compared our method with five clustering methods: k-means [31], DBSCAN [13], DPC [18], DPSLC [32], and LKSM_DPC [33] by using ARI [34], AMI [35] and FMI [36]. ARI (Adjusted Rand Index) is a development of RI (Rand Index), which reflects the degree of overlap between two clusters.…”

Section: Resultsmentioning

confidence: 99%

A Density-Peak-Based Clustering Method for Multiple Densities Dataset

Shi

Yan

et al. 2021

IJGI

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Clustering methods in data mining are widely used to detect hotspots in many domains. They play an increasingly important role in the era of big data. As an advanced algorithm, the density peak clustering (DPC) algorithm is able to deal with arbitrary datasets, although it does not perform well when the dataset includes multiple densities. The parameter selection of cut-off distance dc is normally determined by users’ experience and could affect clustering result. In this study, a density-peak-based clustering method is proposed to detect clusters from datasets with multiple densities and shapes. Two improvements are made regarding the limitations of existing clustering methods. First, DPC finds it difficult to detect clusters in a dataset with multiple densities. Each cluster has a unique shape and the interior includes different densities. This method adopts a step by step merging approach to solve the problem. Second, high densities of points can automatically be selected without manual participation, which is more efficient than the existing methods, which require user-specified parameters. According to experimental results, the clustering method can be applied to various datasets and performs better than traditional methods and DPC.

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Section: Resultsmentioning

confidence: 99%

A Density-Peak-Based Clustering Method for Multiple Densities Dataset

Shi

Yan

et al. 2021

IJGI

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“…Because the DPC algorithm relies heavily on the cut-off distance threshold and the calculation method of local density, it cannot analyze complex manifold data, especially the datasets with uneven density distribution or multiple density peaks in the same cluster. To solve these problems, Ren et al [27] proposed an improved density peaks clustering algorithm based on the layered k-nearest neighbors and subcluster merging (LKSM_DPC) in 2020. Firstly, the algorithm rede ned the local density calculation method by using layered k-nearest neighbors.…”

Section: Dpc Based On the Layered K-nearest Neighbors And Subcluster ...mentioning

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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“…To verify the effectiveness of the CFDPC algorithm, we use 15 commonly used experimental datasets [41]- [44], and select nine artificial datasets from http://cs.joensuu.fi/sipu/datasets/ and six real datasets from http://archive.ics.uci.edu/ml, as listed in Table 1 and Table 2.…”

Section: Experimental Datasetsmentioning

confidence: 99%

A New Density Peak Clustering Algorithm Based on Cluster Fusion Strategy

Zhou

et al. 2022

IEEE Access

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When the density peak clustering algorithm deals with complex datasets and the problem of multiple density peaks in the same cluster, the subjectively selected cluster centers are not accurate enough, and the allocation of non-cluster centers is prone to joint and several errors. To solve the above problems, we propose a new density peak clustering algorithm based on cluster fusion strategy. First, the algorithm screens out the candidate cluster centers by setting two new thresholds to avoid the influence of noise points and outliers. Second, the remaining data points are allocated according to the density peak clustering algorithm to obtain the initial clusters. Third, considering the structural characteristics and spatial distribution of datasets, the new definitions of boundary points, inter-cluster intersection density and intercluster boundary density are provided. To correctly classify the clustering problems with multiple density peaks in the same cluster, a new cluster fusion strategy is proposed, which not only corrects the joint and several errors in the allocation of data points, but also correctly selects the cluster centers. Finally, to test the effectiveness of the proposed clustering algorithm, which is compared with DPC-KNN, DPC, K-means and DBSCAN on nine synthetic datasets and six real datasets. The experimental results demonstrate that the clustering performance of the proposed algorithm outperforms that of other algorithms.INDEX TERMS Clustering; density peaks; candidate cluster center; cluster fusion strategy;

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Effective Density Peaks Clustering Algorithm Based on the Layered K-Nearest Neighbors and Subcluster Merging

Cited by 20 publications

References 30 publications

A Density-Peak-Based Clustering Method for Multiple Densities Dataset

A Density-Peak-Based Clustering Method for Multiple Densities Dataset

An overview on density peaks clustering

A New Density Peak Clustering Algorithm Based on Cluster Fusion Strategy

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