Density peaks clustering using geodesic distances

Du, Mingjing; Ding, Shifei; Xu, Xia; Xue, Yu

doi:10.1007/s13042-017-0648-x

Cited by 53 publications

(31 citation statements)

References 31 publications

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“…The advantage of this approach that it can effectively address variable-density clusters. The concept of geodesic distance was introduced into the DPC algorithm [10] because the traditional DPC algorithm cannot effectively solve multi-manifold structures and data distributions with arbitrary shape clusters.…”

Section: Related Workmentioning

confidence: 99%

Adaptive density peak clustering based on dimensional-free and reverse k-nearest neighbors

Zhang²,

Sun³

et al. 2020

ITC

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Cluster analysis plays a crucial component in consumer behavior segment. The density peak clustering algorithm (DPC) is a novel density-based clustering method. However, it performs poorly in high-dimension datasets and the local density for boundary points. In addition, its fault tolerance is affected by one-step allocation strategy. To overcome these disadvantages, an adaptive density peak clustering algorithm based on dimensional-free and reverse k-nearest neighbors (ERK-DPC) is proposed in this paper. First, we compute Euler cosine distance to obtain the similarity of sample points in high-dimension datasets. Then, the adaptive local density formula is used to measure the local density of each point. Finally, the reverse k-nearest neighbor idea is added on two-step allocation strategy, which assigns the remaining points accurately and effectively. The proposed clustering algorithm is experiments on several benchmark datasets and real-world datasets. By comparing the benchmarks, the results demonstrate that the ERK-DPC algorithm superior to some state-of- the-art methods.

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Section: Related Workmentioning

confidence: 99%

Adaptive density peak clustering based on dimensional-free and reverse k-nearest neighbors

Zhang²,

Sun³

et al. 2020

ITC

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“…For the rest of the sample j, it is classified by DP algorithm as the class cluster of samples, whose density is larger than j and the distance is the nearest to the j [28]. It needs one step to distribute the remaining sample.…”

Section: Density Peak Clustering Algorithmmentioning

confidence: 99%

Improved Density Peak Clustering Algorithm Based on Choosing Strategy Automatically for Cut-off Distance and Cluster Centre

Wang

Han

et al. 2018

Teh. vjesn.

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Due to the defect of quick search density peak clustering algorithm required an artificial attempt to determine the cut-off distance and circle the clustering centres, density peak clustering algorithm based on choosing strategy automatically for cut-off distance and cluster center (CSA-DP) is proposed. The algorithm introduces the improved idea of determining cut-off distance and clustering centres, according to the approximate distance that maximum density sample point to minimum density sample point and the variation of similarity between the points which may be clustering centres. First, obtaining the sample point density according to the k-nearest neighbour samples and tapping the sample sorting of the distance to the maximum density point; then finding the turning position of density trends and determining the cutoff distance on the basis of the turning position; finally, in view of the density peak clustering algorithm, finding the data points which may be the centres of the cluster, comparing the similarity between them and determining the final clustering centres. The simulation results show that the improved algorithm proposed in this paper can automatically determine the cut-off distance, circle the centres, and make the clustering results become more accurate. In the end, this paper makes an empirical analysis on the stock of 147 bio pharmaceutical listed companies by using the improved algorithm, which provides a reliable basis for the classification and evaluation of listed companies. It has a wide range of applicability.

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“…Bie et al [6] proposed a fuzzy-CFSFDP method for adaptively but effectively selecting the cluster centers. Du et al [7] proposed a new DPC algorithm using geodesic distances. And Du et al [8] also proposed a FN-DP (fuzzy neighborhood density peaks) clustering algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Density Peak Clustering Algorithm Based on the K-Nearest Shannon Entropy and Tissue-Like P System

Jiang

Liu

Sun

2019

Mathematical Problems in Engineering

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This study proposes a novel method to calculate the density of the data points based on K-nearest neighbors and Shannon entropy. A variant of tissue-like P systems with active membranes is introduced to realize the clustering process. The new variant of tissue-like P systems can improve the efficiency of the algorithm and reduce the computation complexity. Finally, experimental results on synthetic and real-world datasets show that the new method is more effective than the other state-of-the-art clustering methods.

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Density peaks clustering using geodesic distances

Cited by 53 publications

References 31 publications

Adaptive density peak clustering based on dimensional-free and reverse k-nearest neighbors

Adaptive density peak clustering based on dimensional-free and reverse k-nearest neighbors

Improved Density Peak Clustering Algorithm Based on Choosing Strategy Automatically for Cut-off Distance and Cluster Centre

A Density Peak Clustering Algorithm Based on the K-Nearest Shannon Entropy and Tissue-Like P System

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