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

Jiang, Zhenni; Liu, Xiyu; Sun, Minghe

doi:10.1155/2019/1713801

Cited by 15 publications

(11 citation statements)

References 29 publications

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“…In this study, we use a radius d c to define the neighborhood, and subsequently, calculate the density of a data point [10,15]. Some other studies calculate the density of a data point using the distance to the data point's k nearest neighbor [18][19][20][21]. However, the proper value for either d c or k depends on the characteristic of the dataset.…”

Section: Discussionmentioning

confidence: 99%

“…For example, [16] applied the concept of heat diffusion and [17] employed the potential entropy of the data field to assist in setting the radius . Also, many studies suggested using k nearest neighbors to define density, instead of using the radius [18][19][20][21]. Furthermore, [22] suggested calculating two kinds of densities, one based on k nearest neighbors and one based on local spatial position deviation, to handle datasets with mixed density clusters.…”

Section: Variants Of Dpcmentioning

confidence: 99%

“…[16] applied the concept of heat diffusion and [17] employed the potential entropy of the data field to assist in setting the radius d c . Also, many studies suggested using k nearest neighbors to define density, instead of using the radius d c [18][19][20][21]. Furthermore, ref.…”

Section: Variants Of Dpcmentioning

confidence: 99%

“…A density peak clustering (DPC) algorithm is a density-based clustering method proposed by Rodriguez and Laio [15] in 2014. Since its inception, DPC has received much attention in the research community [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30]. Similar to other density-based methods, DPC calculates the density of each data point in the dataset.…”

Section: Introductionmentioning

confidence: 99%

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Improving Density Peak Clustering by Automatic Peak Selection and Single Linkage Clustering

2020

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Density peak clustering (DPC) is a density-based clustering method that has attracted much attention in the academic community. DPC works by first searching density peaks in the dataset, and then assigning each data point to the same cluster as its nearest higher-density point. One problem with DPC is the determination of the density peaks, where poor selection of the density peaks could yield poor clustering results. Another problem with DPC is its cluster assignment strategy, which often makes incorrect cluster assignments for data points that are far from their nearest higher-density points. This study modifies DPC and proposes a new clustering algorithm to resolve the above problems. The proposed algorithm uses the radius of the neighborhood to automatically select a set of the likely density peaks, which are far from their nearest higher-density points. Using the potential density peaks as the density peaks, it then applies DPC to yield the preliminary clustering results. Finally, it uses single-linkage clustering on the preliminary clustering results to reduce the number of clusters, if necessary. The proposed algorithm avoids the cluster assignment problem in DPC because the cluster assignments for the potential density peaks are based on single-linkage clustering, not based on DPC. Our performance study shows that the proposed algorithm outperforms DPC for datasets with irregularly shaped clusters.

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Section: Discussionmentioning

confidence: 99%

Section: Variants Of Dpcmentioning

confidence: 99%

Section: Variants Of Dpcmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improving Density Peak Clustering by Automatic Peak Selection and Single Linkage Clustering

2020

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“…Various meta-heuristic algorithms, such as GA [69], DE [70] and its variants [71,72], PSO [73,74], ABC [75], and BBO [76], are usually introduced to SNS-based MIEAs as the basic evolutionary operation in the cell or neural [77][78][79][80][81]. The membrane structure in DNS-based MIEAs can be dynamically changed according to communication channel rules, and this class of MIEAs, with an extended membrane structure, has great potential for solving complex problems [82,83]. For another kind of EMC, ADMCMs are designed to overcome the complex programmability of membranebased modes, and the automated synthesis of computing models by applying various meta-heuristic methods can be obtained through this class of EMC [84][85][86].…”

Section: Introductionmentioning

confidence: 99%

An Extended Tissue-like P System Based on Membrane Systems and Quantum-Behaved Particle Swarm Optimization for Image Segmentation

Wang

Liu

et al. 2022

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An extended membrane system using a tissue-like P system with evolutional symport/antiport rules and a promoter/inhibitor, which is based on the evolutionary mechanism of quantum-behaved particle swarm optimization (QPSO) and improved QPSO, named CQPSO-ETP, is designed and developed in this paper. The purpose of CQPSO-ETP is to enhance the optimization performance of statistical network structure-based membrane-inspired evolutionary algorithms (SNS-based MIEAs) and the QPSO technique. In CQPSO-ETP, evolution rules with a promoter based on a standard QPSO mechanism are introduced to evolve objects, and evolution rules with an inhibitor based on an improved QPSO mechanism using self-adaptive selection, and cooperative evolutionary and logistic chaotic mapping methods, are adopted to avoid prematurity. The communication rules with a promoter/inhibitor for objects are introduced to achieve the exchange and sharing of information between different membranes. Under the control of the evolution and communication mechanism, the CQPSO-ETP can effectively improve the performance with the help of a distributed parallel computing model. The proposed CQPSO-ETP is compared with PSO, QPSO and two existing improved QPSO approaches which are conducted on eight classic numerical benchmark functions to verify the effectiveness. Furthermore, computational experiments which are made on eight tested images with three comparative clustering approaches are adopted, and the experimental results demonstrate the clustering validity of the proposed CQPSO-ETP.

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Dynamic Label Propagation Density Peak Clustering Based on the Tissue-Like P Systems

Liu

2023

Lecture Notes in Computer Science

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

Cited by 15 publications

References 29 publications

Improving Density Peak Clustering by Automatic Peak Selection and Single Linkage Clustering

Improving Density Peak Clustering by Automatic Peak Selection and Single Linkage Clustering

An Extended Tissue-like P System Based on Membrane Systems and Quantum-Behaved Particle Swarm Optimization for Image Segmentation

Dynamic Label Propagation Density Peak Clustering Based on the Tissue-Like P Systems

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