An overview on density peaks clustering

Wei, Xiuxi; Peng, Maosong; Huang, Huajuan; Zhou, Yongquan

doi:10.1016/j.neucom.2023.126633

Cited by 12 publications

(6 citation statements)

References 110 publications

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“…DPC was originally proposed by Rodriguez and Laio for data point clustering and has received considerable interest in pattern recognition [26]. In [31] Wei et al presented an overview on DPC to first analyze the theory of DPC and its performance advantages and disadvantages and then summarizes the improvement of DPC in recent years through the improvement effect via experiments and new ideas for improving DPC algorithm in the future. Most recently, Tobin and Zhang [32] provided theoretical properties that characterize DPC and further develops a clustering algorithm, called Component-wise Peak-Finding (CPF) to deal with detection of erroneous points with high density and large distance to points of higher density as well as incoherent cluster assignment caused by noise.…”

Section: Methodsmentioning

confidence: 99%

“…According to (31) and (34), the b c -DPC-BS and k-DPC-BS scores do not take into account the inter-band correlation, and neither do ECA, E-FDPC, IaDPA, or k-SNNC. So, to address this issue, a new concept derived from prominent band peaks proposed in [5], called band prominence value (BPV), BPV(b i ) for each band vector b i is introduced into DPC as a third indicator.…”

Section: Bdpc-bsmentioning

confidence: 99%

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Band Selection via Band Density Prominence Clustering for Hyperspectral Image Classification

Chang,

Kuo,

2024

Remote Sensing

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Band clustering has been widely used for hyperspectral band selection (BS). However, selecting an appropriate band to represent a band cluster is a key issue. Density peak clustering (DPC) provides an effective means for this purpose, referred to as DPC-based BS (DPC-BS). It uses two indicators, cluster density and cluster distance, to rank all bands for BS. This paper reinterprets cluster density and cluster distance as band local density (BLD) and band distance (BD) and also introduces a new concept called band prominence value (BPV) as a third indicator. Combining BLD and BD with BPV derives new band prioritization criteria for BS, which can extend the currently used DPC-BS to a new DPC-BS method referred to as band density prominence clustering (BDPC). By taking advantage of the three key indicators of BDPC, i.e., cut-off band distance bc, k nearest neighboring-band local density, and BPV, two versions of BDPC can be derived called bc-BDPC and k-BDPC, both of which are quite different from existing DPC-based BS methods in three aspects. One is that the parameter bc of bc-BDPC and the parameter k of k-BDPC can be automatically determined by the number of clusters and virtual dimensionality (VD), respectively. Another is that instead of using Euclidean distance, a spectral discrimination measure is used to calculate BD as well as inter-band correlation. The most important and significant aspect is a novel idea that combines BPV with BLD and BD to derive new band prioritization criteria for BS. Extensive experiments demonstrate that BDPC generally performs better than DPC-BS as well as many current state-of-the art BS methods.

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Section: Methodsmentioning

confidence: 99%

Section: Bdpc-bsmentioning

confidence: 99%

Band Selection via Band Density Prominence Clustering for Hyperspectral Image Classification

Chang,

Kuo,

2024

Remote Sensing

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“…The traditional density peak algorithm [20][21][22] is a clustering algorithm used for partitioning data into clusters based on their density, which is capable of handling discrete datasets exhibiting a wide range of shapes, densities, and distributions. The core idea of this algorithm is that several density regions exhibit well-defined boundaries in a dataset.…”

Section: Prerequisite 21 Traditional Density Peak Modelmentioning

confidence: 99%

“…Definition 2 density following distance δ. The density following distance δ of a point represents the distance to the nearest higher-density point [21] and can be defined as follows:…”

Section: Prerequisite 21 Traditional Density Peak Modelmentioning

confidence: 99%

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Enhanced Density Peak-Based Power Grid Reactive Voltage Partitioning

Deng,

Liu,

Liu

et al. 2023

Energies

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Clustering-based reactive voltage partitioning is successful in reducing grid cascading faults, by using clustering methods to categorize different power-consuming entities in the power grid into distinct regions. In reality, each power-consuming entity has different electrical characteristics. Additionally, due to the irregular and uneven distribution of the population, the distribution of electricity consumption is also irregular and uneven. However, the existing method neglects the electrical difference among each entity and the irregular and uneven density distribution of electricity consumption, resulting in poor accuracy and adaptability of these methods. To address these problems, an enhanced density peak model-based power grid reactive voltage partitioning method is proposed in this paper, called EDPVP. First, the power grid is modeled as a weighted reactive network to consider entity electrical differences. Second, the novel local density and density following distance are designed to enhance the density peak model to address the problem that the traditional density peak model cannot adapt to weighted networks. Finally, the enhanced density peak model is further equipped with an optimized cluster centers selection strategy and an updated remaining node assignment strategy, to better identify irregular and uneven density distribution of electricity consumption, and to achieve fast and accurate reactive voltage partition. Experiments on two real power grids demonstrate the effectiveness of the EDPVP.

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Clustering

Ros,

Riad

2023

Unsupervised and Semi-Supervised Learning

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An overview on density peaks clustering

Cited by 12 publications

References 110 publications

Band Selection via Band Density Prominence Clustering for Hyperspectral Image Classification

Band Selection via Band Density Prominence Clustering for Hyperspectral Image Classification

Enhanced Density Peak-Based Power Grid Reactive Voltage Partitioning

Clustering

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