2019
DOI: 10.1002/cpe.5567
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Density peaks clustering based on circular partition and grid similarity

Abstract: Summary In density peaks clustering, its complexity for computing local density and relative distance of samples raises a scalability issue for processing large datasets. To address the issue, density peaks clustering based on circular partition and grid similarity has been proposed. The algorithm partitions the data space into circular grids, with each grid treated as a sample, for determining the number of clusters and searching for the density peaks; then, a new grid similarity is calculated to effectively … Show more

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Cited by 15 publications
(3 citation statements)
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“…Over the past decades, clustering analysis has been serving many applications in the field of machine learning [8][9][10], pattern segmentation [11][12][13][14], and recommendation [15][16][17]. Up to now, different kinds of clustering schemes have been developed, density-based clustering methods [18,19], partition-based clustering methods [20][21][22], hierarchical-based clustering methods [23,24], grid-based clustering methods [25,26], model-based clustering methods [27][28][29], graph-based clustering methods [30,31]and others.…”
Section: Introductionmentioning
confidence: 99%
“…Over the past decades, clustering analysis has been serving many applications in the field of machine learning [8][9][10], pattern segmentation [11][12][13][14], and recommendation [15][16][17]. Up to now, different kinds of clustering schemes have been developed, density-based clustering methods [18,19], partition-based clustering methods [20][21][22], hierarchical-based clustering methods [23,24], grid-based clustering methods [25,26], model-based clustering methods [27][28][29], graph-based clustering methods [30,31]and others.…”
Section: Introductionmentioning
confidence: 99%
“…Density peaks clustering (DPC) (Rodriguez and Laio, 2014) is a density-based clustering algorithm (Xu et al, 2019;Zhao et al, 2020aZhao et al, , 2020b. With DPC, there is no need to designate the number of clusters in advance, and this algorithm can identify clusters of any shapes.…”
Section: Introductionmentioning
confidence: 99%
“…Clustering analysis is the core technology in data mining. Up until now, multifarious clustering algorithms have been proposed, consist of partition‐based methods, 1‐3 layer‐based methods, 4‐6 density‐based methods, 7‐13 grid‐based methods, 14 and so forth. The clustering analysis has been widely used in various fields, including information security, bioinformatics, social network analysis, image pattern recognition, web search 15‐23 .…”
Section: Introductionmentioning
confidence: 99%