scholarly journals Density Peaks Clustering Based on Weighted Local Density Sequence and Nearest Neighbor Assignment

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 34301-34317 ◽  
Author(s):  
Donghua Yu ◽  
Guojun Liu ◽  
Maozu Guo ◽  
Xiaoyan Liu ◽  
Shuang Yao
Keyword(s):  
Nearest Neighbor ◽  
Local Density ◽  
Density Peaks ◽  
Density Peaks Clustering

scholarly journals Density Peaks Clustering based on Nature Nearest Neighbor and Multi-cluster Mergers

2021 ◽  
Author(s):  
Hui Ma ◽  
Ruiqin Wang ◽  
Shuai Yang
Keyword(s):  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Local Density ◽  
Density Peaks ◽  
Density Peaks Clustering ◽  
Assignment Strategy ◽  
Cutoff Distance ◽  
Clustering Effect ◽  
Cluster Mergers ◽  
Selection Of

Abstract Clustering by fast search and find of Density Peaks (DPC) has the advantages of being simple, efficient, and capable of detecting arbitrary shapes, etc. However, there are still some shortcomings: 1) the cutoff distance is specified in advance, and the selection of local density formula will affect the final clustering effect; 2) after the cluster centers are found, the assignment strategy of the remaining points may produce “Domino effect”, that is, once a point is misallocated, more points may be misallocated subsequently. To overcome these shortcomings, we propose a density peaks clustering algorithm based on natural nearest neighbor and multi-cluster mergers. In this algorithm, a weighted local density calculation method is designed by the natural nearest neighbor, which avoids the selection of cutoff distance and the selection of the local density formula. This algorithm uses a new two-stage assignment strategy to assign the remaining points to the most suitable clusters, thus reducing assignment errors. The experiment was carried out on some artificial and real-world datasets. The experimental results show that the clustering effect of this algorithm is better than those other related algorithms.

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

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 174380-174390
Author(s):  
Limin Wang ◽  
Wei Zhou ◽  
Honghuan Wang ◽  
Milan Parmar ◽  
Xuming Han
Keyword(s):  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Assignment Method ◽  
Density Peaks ◽  
Density Peaks Clustering

scholarly journals Adaptive Density Peaks Clustering Based on K-Nearest Neighbor and Gini Coefficient

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 113900-113917
Author(s):  
Dong Jiang ◽  
Wenke Zang ◽  
Rui Sun ◽  
Zehua Wang ◽  
Xiyu Liu
Keyword(s):  
Gini Coefficient ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Density Peaks ◽  
Density Peaks Clustering

scholarly journals Improving K-Nearest Neighbor Approaches for Density-Based Pixel Clustering in Hyperspectral Remote Sensing Images

2020 ◽  
Vol 12 (22) ◽  
pp. 3745
Author(s):  
Claude Cariou ◽  
Steven Le Moan ◽  
Kacem Chehdi
Keyword(s):  
Image Analysis ◽  
Nearest Neighbor ◽  
Hyperspectral Image ◽  
Local Density ◽  
Hyperspectral Images ◽  
K Nearest Neighbor ◽  
Kappa Index ◽  
Graph Regularization ◽  
Hyperspectral Image Analysis ◽  
Density Peaks

We investigated nearest-neighbor density-based clustering for hyperspectral image analysis. Four existing techniques were considered that rely on a K-nearest neighbor (KNN) graph to estimate local density and to propagate labels through algorithm-specific labeling decisions. We first improved two of these techniques, a KNN variant of the density peaks clustering method dpc, and a weighted-mode variant of knnclust, so the four methods use the same input KNN graph and only differ by their labeling rules. We propose two regularization schemes for hyperspectral image analysis: (i) a graph regularization based on mutual nearest neighbors (MNN) prior to clustering to improve cluster discovery in high dimensions; (ii) a spatial regularization to account for correlation between neighboring pixels. We demonstrate the relevance of the proposed methods on synthetic data and hyperspectral images, and show they achieve superior overall performances in most cases, outperforming the state-of-the-art methods by up to 20% in kappa index on real hyperspectral images.

scholarly journals Improved Density Peaks Clustering Based on Natural Neighbor Expanded Group

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Lin Ding ◽  
Weihong Xu ◽  
Yuantao Chen
Keyword(s):  
State Of The Art ◽  
Local Density ◽  
Clustering Technique ◽  
Expanded Group ◽  
Density Peaks ◽  
Natural Neighbor ◽  
Density Peaks Clustering ◽  
Cutoff Distance ◽  
Public Datasets ◽  
Distance Parameter

Density peaks clustering (DPC) is an advanced clustering technique due to its multiple advantages of efficiently determining cluster centers, fewer arguments, no iterations, no border noise, etc. However, it does suffer from the following defects: (1) difficult to determine a suitable value of its crucial cutoff distance parameter, (2) the local density metric is too simple to find out the proper center(s) of the sparse cluster(s), and (3) it is not robust that parts of prominent density peaks are remotely assigned. This paper proposes improved density peaks clustering based on natural neighbor expanded group (DPC-NNEG). The cores of the proposed algorithm contain two parts: (1) define natural neighbor expanded (NNE) and natural neighbor expanded group (NNEG) and (2) divide all NNEGs into a goal number of sets as the final clustering result, according to the closeness degree of NNEGs. At the same time, the paper provides the measurement of the closeness degree. We compared the state of the art with our proposal in public datasets, including several complex and real datasets. Experiments show the effectiveness and robustness of the proposed algorithm.

scholarly journals A Novel Hierarchical Clustering Algorithm Based on Density Peaks for Complex Datasets

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Rong Zhou ◽  
Yong Zhang ◽  
Shengzhong Feng ◽  
Nurbol Luktarhan
Keyword(s):  
Hierarchical Clustering ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Local Density ◽  
Clustering Algorithms ◽  
Complex Structure ◽  
Density Peak ◽  
Global Parameter ◽  
Density Peaks ◽  
Complex Datasets

Clustering aims to differentiate objects from different groups (clusters) by similarities or distances between pairs of objects. Numerous clustering algorithms have been proposed to investigate what factors constitute a cluster and how to efficiently find them. The clustering by fast search and find of density peak algorithm is proposed to intuitively determine cluster centers and assign points to corresponding partitions for complex datasets. This method incorporates simple structure due to the noniterative logic and less few parameters; however, the guidelines for parameter selection and center determination are not explicit. To tackle these problems, we propose an improved hierarchical clustering method HCDP aiming to represent the complex structure of the dataset. A k-nearest neighbor strategy is integrated to compute the local density of each point, avoiding to select the nonnecessary global parameter dc and enables cluster smoothing and condensing. In addition, a new clustering evaluation approach is also introduced to extract a “flat” and “optimal” partition solution from the structure by adaptively computing the clustering stability. The proposed approach is conducted on some applications with complex datasets, where the results demonstrate that the novel method outperforms its counterparts to a large extent.

scholarly journals FUSION OF MULTI-SCALE DEMS FROM DESCENT AND NAVCM IMAGES OF CHANG’E-3 USING COMPRESSED SENSING METHOD

2017 ◽  
Vol XLII-3/W1 ◽  
pp. 119-124
Author(s):  
M. Peng ◽  
W. Wan ◽  
Z. Liu ◽  
K. Di
Keyword(s):  
Sparse Representation ◽  
Input Data ◽  
Local Density ◽  
Matching Pursuit ◽  
Supplementary Information ◽  
Fusion Method ◽  
Multi Scale ◽  
Density Peaks ◽  
Spatial Coverage ◽  
Density Peaks Clustering

The multi-source DEMs generated using the images acquired in the descent and landing phase and after landing contain supplementary information, and this makes it possible and beneficial to produce a higher-quality DEM through fusing the multi-scale DEMs. The proposed fusion method consists of three steps. First, source DEMs are split into small DEM patches, then the DEM patches are classified into a few groups by local density peaks clustering. Next, the grouped DEM patches are used for sub-dictionary learning by stochastic coordinate coding. The trained sub-dictionaries are combined into a dictionary for sparse representation. Finally, the simultaneous orthogonal matching pursuit (SOMP) algorithm is used to achieve sparse representation. We use the real DEMs generated from Chang’e-3 descent images and navigation camera (Navcam) stereo images to validate the proposed method. Through the experiments, we have reconstructed a seamless DEM with the highest resolution and the largest spatial coverage among the input data. The experimental results demonstrated the feasibility of the proposed method.

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