An Improved Density Peak Clustering Algorithm for Multi-Density Data

Yin, Lifeng; Wang, Yingfeng; Chen, Huayue

doi:10.3390/s22228814

Cited by 5 publications

(5 citation statements)

References 40 publications

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“…So, clustering often cannot achieve good results. To resolve this issue, Yin et al [40] extended the DPC algorithm to deal with multi-density data. The cut-off distance d c was selected using KNN to sort the neighbor distances of each data point to draw a line graph of the KNN distance and found the global bifurcation point to divide the data with different densities.…”

Section: Dpc-knnmentioning

confidence: 99%

“…According to [15,16], VD was estimated to be 18. Figures 5 and 6 plot η b c −BDPC i in (39) and η k−BDPC i in (40), which were produced by b c -BDPC and k-BDPC using (a) SID, (b) SAM, and (c) SIDAM for the Purdue Indian Pines scene, respectively, where the red dots are 18 selected bands according to the peaks of BPV, as tabulated in Table 6. In addition, Table 6 also includes 18 bands selected by SMI-BS, ECA, E-FDPC, IaDPI, DPC-kNN, G-DPC-kNN, SNNC, and Uniform BS, where parameters used by various methods are specified in Table 5.…”

Section: Purdue Indian Pinesmentioning

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: Dpc-knnmentioning

confidence: 99%

Section: Purdue Indian Pinesmentioning

confidence: 99%

Band Selection via Band Density Prominence Clustering for Hyperspectral Image Classification

Chang,

Kuo,

2024

Remote Sensing

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“…After ITD decomposition, DPC and the gap-based method [ 26 , 27 , 28 ] is employed to cluster the DOA estimates of all time-frequency points and count the source number of each mode. The DOA estimates of each time-frequency point can be obtained by the conjugate cross-spectrum of the sound pressure and vibration velocity of each mode.…”

Section: Avs Multi-source Detection Algorithm Based On Multimodal Fusionmentioning

confidence: 99%

Acoustic Vector Sensor Multi-Source Detection Based on Multimodal Fusion

Chen

Zhang

Wang

et al. 2023

Sensors

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The direction of arrival (DOA) and number of sound sources is usually estimated by short-time Fourier transform and the conjugate cross-spectrum. However, the ability of a single AVS to distinguish between multiple sources will decrease as the number of sources increases. To solve this problem, this paper presents a multimodal fusion method based on a single acoustic vector sensor (AVS). First, the output of the AVS is decomposed into multiple modes by intrinsic time-scale decomposition (ITD). The number of sources in each mode decreases after decomposition. Then, the DOAs and source number in each mode are estimated by density peak clustering (DPC). Finally, the density-based spatial clustering of applications with the noise (DBSCAN) algorithm is employed to obtain the final source counting results from the DOAs of all modes. Experiments showed that the multimodal fusion method could significantly improve the ability of a single AVS to distinguish multiple sources when compared to methods without multimodal fusion.

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“…Rodriguez and Laio [57] suggest that d cut can chosen such that the average number of neighbors is between 0.01n-0.02n. There are also automatic parameter tuning methods [30,73].…”

Section: Preliminariesmentioning

confidence: 99%

Faster Parallel Exact Density Peaks Clustering

Huang¹,

Yu²,

Shun³

2023

SIAM Conference on Applied and Computational Discrete Algorithms (ACDA23)

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Clustering multidimensional points is a fundamental data mining task, with applications in many fields, such as astronomy, neuroscience, bioinformatics, and computer vision. The goal of clustering algorithms is to group similar objects together. Density-based clustering is a clustering approach that defines clusters as dense regions of points. It has the advantage of being able to detect clusters of arbitrary shapes, rendering it useful in many applications.In this paper, we propose fast parallel algorithms for Density Peaks Clustering (DPC), a popular version of density-based clustering. Existing exact DPC algorithms suffer from low parallelism both in theory and in practice, which limits their application to largescale data sets. Our most performant algorithm, which is based on priority search kd-trees, achieves O(log n log log n) span (parallel time complexity) for a data set of n points. Our algorithm is also work-efficient, achieving a work complexity matching the best existing sequential exact DPC algorithm. In addition, we present another DPC algorithm based on a Fenwick tree that makes fewer assumptions for its average-case complexity to hold.We provide optimized implementations of our algorithms and evaluate their performance via extensive experiments. On a 30core machine with two-way hyperthreading, we find that our best algorithm achieves a 10.8-13169x speedup over the previous best parallel exact DPC algorithm. Compared to the state-of-the-art parallel approximate DPC algorithm, our best algorithm achieves a 1.5-4206x speedup, while being exact.

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An Improved Density Peak Clustering Algorithm for Multi-Density Data

Cited by 5 publications

References 40 publications

Band Selection via Band Density Prominence Clustering for Hyperspectral Image Classification

Band Selection via Band Density Prominence Clustering for Hyperspectral Image Classification

Acoustic Vector Sensor Multi-Source Detection Based on Multimodal Fusion

Faster Parallel Exact Density Peaks Clustering

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