Density peaks clustering based on circular partition and grid similarity

Zhao, Jia; Tang, Jingjing; Fan, Tanghuai; Li, Chenming; Xu, Lizhong

doi:10.1002/cpe.5567

Cited by 15 publications

(3 citation statements)

References 37 publications

(47 reference 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%

Density peaks clustering algorithm with connected local density and punished relative distance

Xiong,

Zang,

Zhao

et al. 2023

J Supercomput

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Density peaks clustering (DPC) algorithm has been widely applied in many fields due to its innovation and efficiency. However, the original DPC algorithm and many of its variants choose Euclidean distance as local density and relative distance estimations, which affects the clustering performance on some specific shaped datasets, such as manifold datasets. To address the above-mentioned issue, we propose a density peak clustering algorithm with connected local density and punished relative distance (DPC-CLD-PRD). Specifically, the proposed approach computes the distance matrix between data pairs using the flexible connectivity distance metric. Then, it calculates the connected local density of each data point via combining the flexible connectivity distance measure and k-nearest neighbor method. Finally, the punished relative distance of each data point is obtained by introducing a connectivity estimation strategy into the distance optimization process. Experiments on synthetic, real-world, and image datasets have demonstrated the effectiveness of the algorithm in this paper.

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Section: Introductionmentioning

confidence: 99%

Density peaks clustering algorithm with connected local density and punished relative distance

Xiong,

Zang,

Zhao

et al. 2023

J Supercomput

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show abstract

“…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%

Density peaks clustering based on geodetic distance and dynamic neighbourhood

LvLi¹,

WangJiayuan²,

WuRunxiu³

et al. 2021

IJBIC

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Density peaks clustering algorithm uses Euclidean distance as a measure of similarity between the samples and it can achieve a good clustering effect when processing the manifold datasets. Utilising this feature, we propose a density peaks clustering algorithm based on geodetic distance and dynamic neighbourhood. This new algorithm measures the similarity between the samples by using geodetic distance, and the number of neighbours K is dynamically adjusted according to the spatial distribution of samples for geodetic distance computation. By choosing geodetic distance as the similarity measure, the problems of manifold dataset clustering can be easily solved, and the clustering is made more effective when the sparse clusters and dense clusters co-exist. The new algorithm was then compared against the other five clustering algorithms on six synthetic datasets and ten real-world datasets. The experiments showed that the proposed algorithm not only outperformed the other conventional algorithms on manifold datasets, but also achieved a very good clustering effect on multi-scale, cluttered and intertwined datasets.

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“…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%

Density peaks clustering based on k‐nearest neighbors sharing

Fan

Yao

Han

et al. 2020

Concurrency and Computation

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Summary The density peaks clustering (DPC) algorithm is a density‐based clustering algorithm. Its density peak depends on the density‐distance model to determine it. The definition of local density for samples used in DPC algorithm only considers distance between samples, while the environments of samples are neglected. This leads to the result that DPC algorithm performs poorly on complex data sets with large difference in density, flow pattern or cross‐winding. In the meantime, the fault tolerance of allocation strategy for samples is relatively poor. Based on the findings, this article proposes a density peaks clustering based on k‐nearest neighbors sharing (DPC‐KNNS) algorithm, which uses the similarity between shared neighbors and natural neighbors to define the local density of samples and the allocation. Comparison between theoretical analysis and experiments on various synthetic and real data reveal that the algorithm proposed in this article can discover the cluster center of complex data sets with large difference in density, flow pattern or cross‐winding. It can also provide effective clustering.

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Density peaks clustering based on circular partition and grid similarity

Cited by 15 publications

References 37 publications

Density peaks clustering algorithm with connected local density and punished relative distance

Density peaks clustering algorithm with connected local density and punished relative distance

Density peaks clustering based on geodetic distance and dynamic neighbourhood

Density peaks clustering based on k‐nearest neighbors sharing

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