Distributed Density Peaks Clustering Revisited

Lü, Jing; Zhao, Yuhai; Tan, Kian-Lee; Wang, Zhengkui

doi:10.1109/tkde.2020.3034611

Cited by 11 publications

(1 citation statement)

References 31 publications

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“…Aiming at the high time complexity of DPC, Lu et al [15] proposed a fast distributed density peaks clustering method based on the Z-value index. is method effectively reduces the time complex from O(n 2 ) to O(n • log n), but the clustering effect is significantly reduced because the data need to be reduced from multidimensional to one dimension.…”

Section: Introductionmentioning

confidence: 99%

A Fast Density Peak Clustering Method with Autoselect Cluster Centers

Wang

et al. 2022

Mobile Information Systems

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Aiming at density peaks clustering needs to manually select cluster centers, this paper proposes a fast new clustering method with auto-select cluster centers. Firstly, our method groups the data and marks each group as core or boundary groups according to its density. Secondly, it determines clusters by iteratively merging two core groups whose distance is less than the threshold and selects the cluster centers at the densest position in each cluster. Finally, it assigns boundary groups to the cluster corresponding to the nearest cluster center. Our method eliminates the need for the manual selection of cluster centers and improves clustering efficiency with the experimental results.

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Section: Introductionmentioning

confidence: 99%

A Fast Density Peak Clustering Method with Autoselect Cluster Centers

Wang

et al. 2022

Mobile Information Systems

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DPSPC: A Density Peak-Based Statistical Parallel Clustering Algorithm for Big Data

Pan,

Deng,

Yang

et al. 2024

Knowledge Science, Engineering and Management

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A comprehensive survey of fast graph clustering

Xue,

Xing,

Wang

et al. 2024

Vicinagearth

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Graph clustering methods are popular due to their ability to discover clusters with arbitrary shapes. However, with the emergence of large-scale datasets, the efficiency of graph clustering algorithms has become a significant concern. As a result, many researchers have been drawn to the field of fast graph clustering algorithms, leading to rapid and intricate advancements in related research. Nevertheless, there is currently no comprehensive survey available for fast graph clustering algorithms. To address this gap, we review these fast graph clustering models in both single and multi-view fields, categorizing them based on different properties and analyzing their advantages and disadvantages. In the single-view field, the main categories we explore include large graph methods and bipartite graph methods. The former includes graph cut and graph density methods, while the latter includes graph cut, co-clustering, and label transmission methods. For the multi-view field, the main categories also include large graph methods and bipartite graph methods. The former is specifically designed to avoid the eigenvalue decomposition of graph cut models, and the latter focuses on accelerating algorithms by integrating anchor points. Towards the conclusion of this paper, we discuss the challenges and provide several further research directions for fast graph clustering.

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Distributed Density Peaks Clustering Revisited

Cited by 11 publications

References 31 publications

A Fast Density Peak Clustering Method with Autoselect Cluster Centers

A Fast Density Peak Clustering Method with Autoselect Cluster Centers

DPSPC: A Density Peak-Based Statistical Parallel Clustering Algorithm for Big Data

A comprehensive survey of fast graph clustering

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