Automatic clustering based on density peak detection using generalized extreme value distribution

Ding, Jiajun; He, Xiongxiong; Yuan, Junqing; Jiang, Bo‐Nan

doi:10.1007/s00500-017-2748-7

Cited by 28 publications

(18 citation statements)

References 27 publications

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“…Chebyshev inequality [18] is adopted to set an upper bound for selecting the nodes with abnormally large γ as community center points, and unique labels are assigned to the center points for label propagation in the next step.…”

Section: Based On Imentioning

confidence: 99%

RefineDCN: An Improved Community Detection Algorithm Based on Center Finding

Tang

Wang

2021

IEEE Access

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Section: Based On Imentioning

confidence: 99%

RefineDCN: An Improved Community Detection Algorithm Based on Center Finding

Tang

Wang

2021

IEEE Access

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“…Then, the remaining points are assigned by their nearest neighbor of higher density. Inspired by the visual selection rule of DPC, reference [30] proposed a judgment index that approximately follows the generalized extreme value (GEV) distribution, and each cluster center's judgment index is much higher. Hence, it is reasonable that points are selected as cluster centers if their judgment indexes are larger than the upper quantile of GEV.…”

Section: Related Workmentioning

confidence: 99%

A Novel Clustering Algorithm Based on DPC and PSO

et al. 2020

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Analyzing the fast search and find of density peaks clustering (DPC) algorithm, we find that the cluster centers cannot be determined automatically and that the selected cluster centers may fall into a local optimum and the random selection of the parameter cutoff distance d c value. To overcome these problems, a novel clustering algorithm based on DPC & PSO (PDPC) is proposed. Particle swarm optimization (PSO) is introduced because of its simple concept and strong global search ability, which can find the optimal solution in relatively few iterations. First, to solve the effect of the selection of the parameter d c on the calculation density and the clustering results, this paper proposes a method to calculate that parameter. Second, a new fitness criterion function is proposed that iteratively searches K global optimal solutions through the PSO algorithm, that is, the initial cluster centers. Third, each sample is assigned to K initial center points according to the minimum distance principle. Finally, we update the cluster centers and redistribute the remaining objects to the clusters closest to the cluster centers. Furthermore, the effectiveness of the proposed algorithm is verified on nine typical benchmark data sets. The experimental results show that the PDPC can effectively solve the problem of cluster center selection in the DPC algorithm, avoiding the subjectivity of the manual selection process and overcoming the influence of the parameter d c. Compared with the other six algorithms, the PDPC algorithm has a stronger global search ability, higher stability and a better clustering effect. INDEX TERMS Clustering, density peak, particle swarm optimization, fitness function.

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“…Rong et al also combined the local density approach with an improved hierarchical clustering algorithm to improve the clustering process [36], but if clusters of dataset overlap are higher, incorrect clusters may be produced. Ding et al proposed DPC-GEV and DPC-CI to automatically identify clustering centers based on the generalized extreme value and Chebyshev's inequality, respectively [37], but this method cannot be applied to datasets with high overlapping. Chen analyzed and extracted the information of data objects using the normal distribution theory, excluded the abnormal objects, and then identified the clustering centers [38].…”

Section: Detection Of Clustering Centers Based On Cfsfdpmentioning

confidence: 99%

Automatic Determination of Clustering Centers for “Clustering by Fast Search and Find of Density Peaks”

Min

Huang

Sheng

2020

Mathematical Problems in Engineering

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Dividing abstract object sets into multiple groups, called clustering, is essential for effective data mining. Clustering can find innate but unknown real-world knowledge that is inaccessible by any other means. Rodriguez and Laio have published a paper about a density-based fast clustering algorithm in Science called CFSFDP. CFSFDP is a highly efficient algorithm that clusters objects by using fast searching of density peaks. But with CFSFDP, the essential second step of finding clustering centers must be done manually. Furthermore, when the amount of data objects increases or a decision graph is complicated, determining clustering centers manually is difficult and time consuming, and clustering accuracy reduces sharply. To solve this problem, this paper proposes an improved clustering algorithm, ACDPC, that is based on data detection, which can automatically determinate clustering centers without manual intervention. First, the algorithm calculates the comprehensive metrics and sorts them based on the CFSFDP method. Second, the distance between the sorted objects is used to judge whether they are the correct clustering centers. Finally, the remaining objects are grouped into clusters. This algorithm can efficiently and automatically determine clustering centers without calculating additional variables. We verified ACDPC using three standard datasets and compared it with other clustering algorithms. The experimental results show that ACDPC is more efficient and robust than alternative methods.

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Automatic clustering based on density peak detection using generalized extreme value distribution

Cited by 28 publications

References 27 publications

RefineDCN: An Improved Community Detection Algorithm Based on Center Finding

RefineDCN: An Improved Community Detection Algorithm Based on Center Finding

A Novel Clustering Algorithm Based on DPC and PSO

Automatic Determination of Clustering Centers for “Clustering by Fast Search and Find of Density Peaks”

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