HCFS: A Density Peak Based Clustering Algorithm Employing A Hierarchical Strategy

Zhuo, Linlin; Li, Kenli; Liao, Bo; Li, Xiaoxue; Li, Keqin

doi:10.1109/access.2019.2918772

Cited by 13 publications

(6 citation statements)

References 21 publications

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“…This paper builds several sets of experiments, and the results indicate that the density peak based algorithm (CFS) [26] has higher performance than the spectral clustering algorithm, although CFS has no rules to avoid the difficulties caused by selecting the ''qualified'' center point, and it doesn't take into account the non-uniform distribution in the data set. Zhuo et al proposed a method that can solve this problem [5], excluding it is only applicable to low-dimensional data sets. However, the non-uniform distribution within the cluster will also be common in highdimensional data sets.…”

Section: Related Workmentioning

confidence: 99%

“…Obviously, it could be seen that the time cost in first step of the experiment is much smaller than the second and third steps, which could be almost ignored. Furthermore, in the second step, the complexity of constructing distance matrix is N * (N − 1) * k, where N is the number of data points and k is the number of features, while the third step could be drawn from [5].…”

Section: Time Complexity Analysismentioning

confidence: 99%

“…This inspired us to propose the density peak algorithm based on multi-view and tensor low rank expression MVTLR-HCFS, where MVTLR stands for multi view combined with tensor low rank representation. Besides, HCFS means hierarchical strategy enabled clustering by fast search, which is a kind of algorithm that we have proposed in [5]. Above all, the current single-view subspace clustering algorithm is susceptible to the accuracy of the view, so the multi-view scheme is adopted to integrate the multi-view into a tensor, which is obtained through the optimization of the tensor for solving the low-rank optimization problem of tensor, and then catch the subspace expression of each view.…”

Section: Introductionmentioning

confidence: 99%

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MVTLR-HCFS: Density Peak Algorithm Based on Multi-View and Tensor Low Rank Expression

Yang

Zhuo

Zeng³

et al. 2021

IEEE Access

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With the rapid development of hardware and software technology, modern industry has produced a large amount of high-dimensional unlabeled data, such as pictures and videos. As clusters of these data sets may exist in some subspaces, traditional algorithms are no longer applicable. The related algorithms based on sparse subspace can find clusters in the subspace, which solves the problem of high data dimension. However, their clustering process based on only one single feature, which results in their performance being particularly sensitive to this single view. Affected by the integrated algorithm, a large number of multi-view methods began to emerge. These methods improve the clustering performance by integrating the subspace expressions of multiple views, but the problem is that the complementary information of multiple views cannot be fully considered. In addition, the problem of non-uniform distribution in clusters also exists in high-dimensional data sets. In this paper, based on the multi-view subspace clustering method, a clustering algorithm based on tensor low rank expression is proposed to solve the clustering problem of high-dimensional datasets. On the one hand, this paper solves the problem of noise and data corruption by combining the method of 2,1 norms, which transform the optimization problem of solving multi-view subspace expression into a low-rank expression problem of tensor to fully consider the complementarities between views. On the other hand, the proposed scheme solves the problem of non-uniform distribution in clusters in high-dimensional data by combining with the density peak algorithm based on hierarchical strategy. Experiment results show the effectiveness of the algorithm. INDEX TERMS Cluster, density peak algorithm, subspace, complementary information.

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Section: Related Workmentioning

confidence: 99%

Section: Time Complexity Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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MVTLR-HCFS: Density Peak Algorithm Based on Multi-View and Tensor Low Rank Expression

Yang

Zhuo

Zeng³

et al. 2021

IEEE Access

Self Cite

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“…The dataset is assigned to the users on the experience and the priority of cut-off distance. The DPC assignment will be assigning the centre points by the local density of neighbour cluster it will be done in the error propagation of the label cluster [4]. The clustering and recombination levels of the network are improved taking into account factors including the remaining energy of the nodes and the distance between the nodes [5].…”

Section: Introductionmentioning

confidence: 99%

An Energy Effectual Swarm Based Hierarchical Density Peak Clustering (HDPCS) Method for Multi-sensor Data Fusion Strategy

Brumancia¹,

Samuel²

2021

IJIES

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Clustering is the most frequently used physical architecture in data fusion, causing a problem as it has random distribution within local clusters, we propose a density peak based clustering algorithm utilizing a hierarchical approach. As a proficient method to joint decision-making of multi sensors, data fusion contains several benefits in data mining. In this work, we use the Salp Swarm Optimizer (SSO) algorithm based Hierarchical Density Peak Clustering (DPC) method to cluster the multiple sensor nodes as per the data similitude for the process of data fusion. Here, the higher density, distance and the cut off values are determined by using the SSO algorithm and then the chosen optimal fixed values were used to further process. Hierarchical Density Peak Clustering estimates the density and distance of each point. It is validated experimentally and compared it with the datasets and the error prediction with the test case numbers. The experimental results of the method are 96% accuracy determined from different datasets.

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“…Many researchers have addressed the problem that the DPC has difficulties dealing with complex structured datasets. Zhuo [21] confronted the uneven distribution within local clusters and proposed a density peaks clustering algorithm employing a hierarchical strategy (HCFS). The HCFS used a new mechanism to measure the similarity and connectivity of subclusters, which combined highly similar and interconnected subclusters into a cluster.…”

Section: Introductionmentioning

confidence: 99%

Effective Density Peaks Clustering Algorithm Based on the Layered K-Nearest Neighbors and Subcluster Merging

Ren

Sun

et al. 2020

IEEE Access

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Density peaks clustering (DPC) algorithm is a novel density-based clustering algorithm, which is simple and efficient, is not necessary to specify the number of clusters in advance, and can find any nonspherical class clusters. However, DPC relies heavily on the calculation methods of the cutoff distance threshold and local density and cannot analyze complex manifold data, especially datasets with uneven density distribution and multiple peaks in the same cluster. To solve these problems, we propose an improved density peaks clustering algorithm based on the layered k-nearest neighbors and subcluster merging (LKSM_DPC). First, we redefine the local density calculation method using the layered k-nearest neighbors. To adapt to datasets with different densities, the k-nearest neighbors are divided into multiple layers. Second, for the multiple peaks in the same cluster problem, we design a new mechanism to calculate the similarity of subclusters based on the idea of shared neighbors and Newton's law of gravitation, and a subcluster merging strategy is proposed. To prove the effectiveness of our algorithm, we compare the LKSM_DPC with K-means, DBSCAN, DPC, and DPC derivatives for 24 datasets. A large number of experiments demonstrate that our algorithm can often outperform other algorithms.

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HCFS: A Density Peak Based Clustering Algorithm Employing A Hierarchical Strategy

Cited by 13 publications

References 21 publications

MVTLR-HCFS: Density Peak Algorithm Based on Multi-View and Tensor Low Rank Expression

MVTLR-HCFS: Density Peak Algorithm Based on Multi-View and Tensor Low Rank Expression

An Energy Effectual Swarm Based Hierarchical Density Peak Clustering (HDPCS) Method for Multi-sensor Data Fusion Strategy

Effective Density Peaks Clustering Algorithm Based on the Layered K-Nearest Neighbors and Subcluster Merging

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