Density peaks clustering with gap-based automatic center detection

Flores, Kathia G.; Garza, Sara E.

doi:10.1016/j.knosys.2020.106350

Cited by 39 publications

(14 citation statements)

References 58 publications

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“…The third aspect is to determine the clustering centers. Flores et al [26] automatically determined cluster centers by the spacing between data points in a one-dimensional decision graph. Lv et al [27] calculated the difference change between the decision values, and automatically obtained the cluster centers according to the position of the inflection point.…”

Section: Related Workmentioning

confidence: 99%

A New Density Peak Clustering Algorithm Based on Cluster Fusion Strategy

Zhou

et al. 2022

IEEE Access

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When the density peak clustering algorithm deals with complex datasets and the problem of multiple density peaks in the same cluster, the subjectively selected cluster centers are not accurate enough, and the allocation of non-cluster centers is prone to joint and several errors. To solve the above problems, we propose a new density peak clustering algorithm based on cluster fusion strategy. First, the algorithm screens out the candidate cluster centers by setting two new thresholds to avoid the influence of noise points and outliers. Second, the remaining data points are allocated according to the density peak clustering algorithm to obtain the initial clusters. Third, considering the structural characteristics and spatial distribution of datasets, the new definitions of boundary points, inter-cluster intersection density and intercluster boundary density are provided. To correctly classify the clustering problems with multiple density peaks in the same cluster, a new cluster fusion strategy is proposed, which not only corrects the joint and several errors in the allocation of data points, but also correctly selects the cluster centers. Finally, to test the effectiveness of the proposed clustering algorithm, which is compared with DPC-KNN, DPC, K-means and DBSCAN on nine synthetic datasets and six real datasets. The experimental results demonstrate that the clustering performance of the proposed algorithm outperforms that of other algorithms.INDEX TERMS Clustering; density peaks; candidate cluster center; cluster fusion strategy;

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Section: Related Workmentioning

confidence: 99%

A New Density Peak Clustering Algorithm Based on Cluster Fusion Strategy

Zhou

et al. 2022

IEEE Access

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“…Lv et al [28] proposed a method to determine the cluster centers automatically according to the decision value defined by the product of local density and relative-separation. Flores et al [29] proposed a strategy to find cluster centers adaptively by searching the gaps among data points on the one-dimensional decision graph mapped. Lin et al [30] introduced a hyper-parameter, neighbor radius, to select a group of possible density peaks, as preliminary clustering results.…”

Section: Introductionmentioning

confidence: 99%

A Density Peaks Clustering Algorithm With Sparse Search and K-d Tree

Shan

et al. 2022

IEEE Access

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Density peaks clustering has become a nova of clustering algorithm because of its simplicity and practicality. However, there is one main drawback: it is time-consuming due to its high computational complexity. Herein, a density peaks clustering algorithm with sparse search and K-d tree is developed to solve this problem. Firstly, a sparse distance matrix is calculated by using K-d tree to replace the original full rank distance matrix, so as to accelerate the calculation of local density. Secondly, a sparse search strategy is proposed to accelerate the computation of relative-separation with the intersection between the set of k nearest neighbors and the set consisting of the data points with larger local density for any data point. Furthermore, a second-order difference method for decision values is adopted to determine the cluster centers adaptively. Finally, experiments are carried out on datasets with different distribution characteristics, by comparing with other six state-of-the-art clustering algorithms. It is proved that the algorithm can effectively reduce the computational complexity of the original DPC from O(n 2 K) to O(n(n 1−1/K + k)). Especially for larger datasets, the efficiency is elevated more remarkably. Moreover, the clustering accuracy is also improved to a certain extent. Therefore, it can be concluded that the overall performance of the newly proposed algorithm is excellent.INDEX TERMS Density peaks clustering, sparse search strategy, K-d tree, computational complexity, second-order difference method.

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“…Aiming at the problem that DPC needs manual participation in selecting cluster centers, Flores et al [13] proposed a density peaks clustering with a gap-based automatic center detection method. is method calculates a threshold to distinguish between cluster center samples and noncenter samples.…”

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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Density peaks clustering with gap-based automatic center detection

Cited by 39 publications

References 58 publications

A New Density Peak Clustering Algorithm Based on Cluster Fusion Strategy

A New Density Peak Clustering Algorithm Based on Cluster Fusion Strategy

A Density Peaks Clustering Algorithm With Sparse Search and K-d Tree

A Fast Density Peak Clustering Method with Autoselect Cluster Centers

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