Boundary Detection-Based Density Peaks Clustering

Qiao, Dianfeng; Liang, Yan; Jiao, Lianmeng

doi:10.1109/access.2019.2947640

Cited by 7 publications

(3 citation statements)

References 23 publications

(24 reference statements)

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“…The main idea of density-based clustering algorithm is to take the data point as the center to calculate the local density of all data points and the distance between two data points, and connects high-density data points through the collection of local density and distance [42]. It regards high-density region as cluster and low-density region as noise to complete clustering [43].…”

Section: Introductionmentioning

confidence: 99%

Interference-Suppressed and Cluster-Optimized Hyperspectral Target Extraction Based on Density Peak Clustering

Shang

Yang

Han

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Target extraction can provide a prior knowledge for spectral unmixing, unsupervised hyperspectral image classification and unsupervised target detection tasks, which is of great practice. Considering that the traditional endmember extraction algorithms such as automatic target generation process (ATGP) and simplex growing algorithm (SGA) are sensitive to the burst noise caused by hyperspectral sensor imaging, and cannot extract the appropriate target set to simultaneously guide the various tasks, a weighted target extraction algorithm based on the density peak clustering (DPC) is proposed in this paper. Firstly, DPC is extended to hyperspectral images, and the decision graph is generated according to the density and distance of each pixel to effectively classify the regions with different attributes like burst noise, cluster center, cluster boundary and cluster core. On the basis, three DPC-based weighted vectors are designed, and the weighted target extraction algorithms W-ATGP and W-SGA are put forward which allocates a lower weight than the core pixel to the interference so as to reduce its impact on ATGP and SGA while obtain the high-quality targets for various tasks. The experimental results on three datasets show that the accuracies of the target sets extracted by W-ATGP and W-SGA are higher than that of ATGP and SGA, no matter as the prior knowledge of spectral unmixing, unsupervised hyperspectral image classification or unsupervised target detection, which verifies the effectiveness of the proposed methods.Index Terms-hyperspectral image, density peak clustering, weighted target extraction, spectral unmixing, unsupervised hyperspectral image classification, unsupervised target detection.

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Section: Introductionmentioning

confidence: 99%

Interference-Suppressed and Cluster-Optimized Hyperspectral Target Extraction Based on Density Peak Clustering

Shang

Yang

Han

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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show abstract

“…He proposed an improved density peaks clustering algorithm based on shared-neighbors between local cores (LORE-DP) and redefined natural neighbor-based density and the newly defined graph-based distance. Qiao [26] studied the problem that DPC is not highly effective for the division of unevenly distributed data. He proposed boundary detectionbased density peaks clustering (BDDPC) and introduced a new indicator named the asymmetry measure that enhanced the ability to find boundary points.…”

Section: Introductionmentioning

confidence: 99%

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

Ren

Sun

et al. 2020

IEEE Access

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Density peaks clustering (DPC) algorithm is a novel density-based clustering algorithm, which is simple and efficient, is not necessary to specify the number of clusters in advance, and can find any nonspherical class clusters. However, DPC relies heavily on the calculation methods of the cutoff distance threshold and local density and cannot analyze complex manifold data, especially datasets with uneven density distribution and multiple peaks in the same cluster. To solve these problems, we propose an improved density peaks clustering algorithm based on the layered k-nearest neighbors and subcluster merging (LKSM_DPC). First, we redefine the local density calculation method using the layered k-nearest neighbors. To adapt to datasets with different densities, the k-nearest neighbors are divided into multiple layers. Second, for the multiple peaks in the same cluster problem, we design a new mechanism to calculate the similarity of subclusters based on the idea of shared neighbors and Newton's law of gravitation, and a subcluster merging strategy is proposed. To prove the effectiveness of our algorithm, we compare the LKSM_DPC with K-means, DBSCAN, DPC, and DPC derivatives for 24 datasets. A large number of experiments demonstrate that our algorithm can often outperform other algorithms.

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“…The core idea of DPC is that cluster centers are characterized by a higher density and a relatively longer distance. The outstanding performance of DPC has attracted many scholars' attention, and many variants based on DPC have been proposed to address various clustering problems, such as BDDPC [20], and DPC-KNN [21]. In this paper, the original DPC algorithm is shown to be able to handle the type of dataset that contains observations of targets that obey a zero-Gaussian distribution well.…”

Section: Introductionmentioning

confidence: 99%

A Robust Multi-Sensor Data Fusion Clustering Algorithm Based on Density Peaks

Fan

Xie

2019

Sensors

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In this paper, a novel multi-sensor clustering algorithm, based on the density peaks clustering (DPC) algorithm, is proposed to address the multi-sensor data fusion (MSDF) problem. The MSDF problem is raised in the multi-sensor target detection (MSTD) context and corresponds to clustering observations of multiple sensors, without prior information on clutter. During the clustering process, the data points from the same sensor cannot be grouped into the same cluster, which is called the cannot link (CL) constraint; the size of each cluster should be within a certain range; and overlapping clusters (if any) must be divided into multiple clusters to satisfy the CL constraint. The simulation results confirm the validity and reliability of the proposed algorithm.

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Boundary Detection-Based Density Peaks Clustering

Cited by 7 publications

References 23 publications

Interference-Suppressed and Cluster-Optimized Hyperspectral Target Extraction Based on Density Peak Clustering

Interference-Suppressed and Cluster-Optimized Hyperspectral Target Extraction Based on Density Peak Clustering

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

A Robust Multi-Sensor Data Fusion Clustering Algorithm Based on Density Peaks

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