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Generalized density clustering
The Annals of Statistics, 2010 We study generalized density-based clustering in which sharply defined clusters such as clusters on lower-dimensional manifolds are allowed. We show that accurate clustering is possible even in high dimensions.
Rinaldo, Alessandro, Wasserman, Larry
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Quantum Density Peak Clustering Algorithm [PDF]
Entropy, 2022 A widely used clustering algorithm, density peak clustering (DPC), assigns different attribute values to data points through the distance between data points, and then determines the number and range of clustering by attribute values.
Zhihao Wu, Tingting Song, Yanbing Zhang
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Skeleton Clustering: Dimension-Free Density-Aided Clustering
Journal of the American Statistical Association, 2023 We introduce a density-based clustering method called skeleton clustering that can detect clusters in multivariate and even high-dimensional data with irregular shapes. To bypass the curse of dimensionality, we propose surrogate density measures that are less dependent on the dimension but have intuitive geometric interpretations.
Zeyu Wei, Yen-Chi Chen
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Density‐based clustering [PDF]
WIREs Data Mining and Knowledge Discovery, 2019 AbstractClustering refers to the task of identifying groups or clusters in a data set. In density‐based clustering, a cluster is a set of data objects spread in the data space over a contiguous region of high density of objects. Density‐based clusters are separated from each other by contiguous regions of low density of objects. Data objects located in
Ricardo J. G. B. Campello +3 more
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VDPC: Variational density peak clustering algorithm [PDF]
Information Sciences, 2021 <div>Clustering is an important unsupervised knowledge acquisition method, which divides the unlabeled data into different groups \cite{atilgan2021efficient,d2021automatic}. Different clustering algorithms make different assumptions on the cluster formation, thus, most clustering algorithms are able to well handle at least one particular type of ...
Wang, Yizhang +4 more
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Deep density-based image clustering [PDF]
Knowledge-Based Systems, 2020 Recently, deep clustering, which is able to perform feature learning that favors clustering tasks via deep neural networks, has achieved remarkable performance in image clustering applications. However, the existing deep clustering algorithms generally need the number of clusters in advance, which is usually unknown in real-world tasks.
Ren, Yazhou +3 more
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European Journal of Remote Sensing, 2020 This article proposes the tracking algorithm based on density clustering of time scale and mesoscale eddy of Kalman filtering using the fused SLA data of altimeter.
Ji-Tao Li, Yong-Quan Liang
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Clustering Algorithm Based on Density Peak and Neighbor Optimization
Jisuanji kexue yu tansuo, 2020 The time complexity of density peak algorithm in selecting the cluster center is very high. It needs to manually select the cutoff distance. When processing the manifold data, there may be multiple density peaks, which leads to the decrease of clustering
HE Yunbin, DONG Heng, WAN Jing, LI Song
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An Adaptive CSP and Clustering Classification for Online Motor Imagery EEG
IEEE Access, 2020 A potential limitation of motor imagery (MI) based brain-computer interface (BCI) (MI-BCI) is that it usually requires a relatively long time to record sufficient electroencephalogram (EEG) data for robust feature extraction and classification. Moreover,
Qin Jiang +3 more
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Density Peak Clustering Algorithm Based on Relative Density [PDF]
Jisuanji gongcheng, 2023 When the density peak clustering algorithm deals with datasets with uneven density，it is easy to divide the low-density clusters into high-density clusters，divide the high-density clusters into multiple sub-clusters，and exists the error propagation ...
WEI Ya, ZHANG Zhengjun, HE Kailin, TANG Li
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