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Ren Q, Zhang L, Liu S, Liu JX, Shang J, Liu X. A Delayed Spiking Neural Membrane System for Adaptive Nearest Neighbor-Based Density Peak Clustering. Int J Neural Syst 2024:2450050. [PMID: 38973024 DOI: 10.1142/s0129065724500503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/09/2024]
Abstract
Although the density peak clustering (DPC) algorithm can effectively distribute samples and quickly identify noise points, it lacks adaptability and cannot consider the local data structure. In addition, clustering algorithms generally suffer from high time complexity. Prior research suggests that clustering algorithms grounded in P systems can mitigate time complexity concerns. Within the realm of membrane systems (P systems), spiking neural P systems (SN P systems), inspired by biological nervous systems, are third-generation neural networks that possess intricate structures and offer substantial parallelism advantages. Thus, this study first improved the DPC by introducing the maximum nearest neighbor distance and K-nearest neighbors (KNN). Moreover, a method based on delayed spiking neural P systems (DSN P systems) was proposed to improve the performance of the algorithm. Subsequently, the DSNP-ANDPC algorithm was proposed. The effectiveness of DSNP-ANDPC was evaluated through comprehensive evaluations across four synthetic datasets and 10 real-world datasets. The proposed method outperformed the other comparison methods in most cases.
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Affiliation(s)
- Qianqian Ren
- School of Computer Science, Qufu Normal University, Rizhao 276826, P. R. China
| | - Lianlian Zhang
- School of Computer Science, Qufu Normal University, Rizhao 276826, P. R. China
| | - Shaoyi Liu
- School of Computer Science, Qufu Normal University, Rizhao 276826, P. R. China
| | - Jin-Xing Liu
- School of Computer Science, Qufu Normal University, Rizhao 276826, P. R. China
| | - Junliang Shang
- School of Computer Science, Qufu Normal University, Rizhao 276826, P. R. China
| | - Xiyu Liu
- Academy of Management Science, Business School, Shandong Normal University, Jinan 250300, P. R. China
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Ma J, Hao Z, Hu M. TMsDP: two-stage density peak clustering based on multi-strategy optimization. DATA TECHNOLOGIES AND APPLICATIONS 2022. [DOI: 10.1108/dta-08-2021-0222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PurposeThe density peak clustering algorithm (DP) is proposed to identify cluster centers by two parameters, i.e. ρ value (local density) and δ value (the distance between a point and another point with a higher ρ value). According to the center-identifying principle of the DP, the potential cluster centers should have a higher ρ value and a higher δ value than other points. However, this principle may limit the DP from identifying some categories with multi-centers or the centers in lower-density regions. In addition, the improper assignment strategy of the DP could cause a wrong assignment result for the non-center points. This paper aims to address the aforementioned issues and improve the clustering performance of the DP.Design/methodology/approachFirst, to identify as many potential cluster centers as possible, the authors construct a point-domain by introducing the pinhole imaging strategy to extend the searching range of the potential cluster centers. Second, they design different novel calculation methods for calculating the domain distance, point-domain density and domain similarity. Third, they adopt domain similarity to achieve the domain merging process and optimize the final clustering results.FindingsThe experimental results on analyzing 12 synthetic data sets and 12 real-world data sets show that two-stage density peak clustering based on multi-strategy optimization (TMsDP) outperforms the DP and other state-of-the-art algorithms.Originality/valueThe authors propose a novel DP-based clustering method, i.e. TMsDP, and transform the relationship between points into that between domains to ultimately further optimize the clustering performance of the DP.
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Liang B, Cai J, Yang H. Grid-DPC: Improved density peaks clustering based on spatial grid walk. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03705-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Zeng S, Wang Q, Wang S, Liu P. Shadow detection of soil image based on density peak clustering and histogram fitting. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-211633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Shadow detection is a significant preprocessing work that soil type is classified with machine vision. Thus, Density peak clustering based on histogram fitting(DPCHF) is proposed to segment soil image shadows. First, its clustering centers are adaptively obtained by constructing a new parameterless density formula and decision value measure. Then the Fourier series are drawn into it to approximate the gray histogram and a part of gray-levels are allocated by valley points of the histogram fitting curve. Finally, an optimization model is established to optimize the threshold of detecting the shadow in the soil image, and the remaining gray-levels are clustered by the threshold. The simulation results show that DPCHF is better than the contrast algorithm. The average brightness standard deviations of the shadow and non-shadow are respectively 20.9348 and 20.3081 with DPCHF. It can realize the adaptive shadow detection of soil images and there is not the “domino” error propagation in it.
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Affiliation(s)
- Shaohua Zeng
- College of Computer and Information Science, Chongqing Normal University, Chongqing, China
- Chongqing Center of Engineering Technology Research on Digital Agricultural Service, Chongqing, China
| | - Qi Wang
- College of Computer and Information Science, Chongqing Normal University, Chongqing, China
- Chongqing Center of Engineering Technology Research on Digital Agricultural Service, Chongqing, China
| | - Shuai Wang
- The Master Station of Agricultural Technology Promotion, Chongqing Agricultural and Rural Committee, Chongqing, China
| | - Ping Liu
- The Center of Agricultural Technology Promotion, Agricultural and Rural Committee of Shapingba District, Chongqing, China
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Platero-Rochart D, González-Alemán R, Hernández-Rodríguez EW, Leclerc F, Caballero J, Montero-Cabrera L. RCDPeaks: memory-efficient density peaks clustering of long molecular dynamics. Bioinformatics 2022; 38:1863-1869. [PMID: 35020783 DOI: 10.1093/bioinformatics/btac021] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 12/06/2021] [Accepted: 01/07/2022] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Density Peaks is a widely spread clustering algorithm that has been previously applied to Molecular Dynamics (MD) simulations. Its conception of cluster centers as elements displaying both a high density of neighbors and a large distance to other elements of high density, particularly fits the nature of a geometrical converged MD simulation. Despite its theoretical convenience, implementations of Density Peaks carry a quadratic memory complexity that only permits the analysis of relatively short trajectories. RESULTS Here, we describe DP+, an exact novel implementation of Density Peaks that drastically reduces the RAM consumption in comparison to the scarcely available alternatives designed for MD. Based on DP+, we developed RCDPeaks, a refined variant of the original Density Peaks algorithm. Through the use of DP+, RCDPeaks was able to cluster a one-million frames trajectory using less than 4.5 GB of RAM, a task that would have taken more than 2 TB and about 3× more time with the fastest and less memory-hunger alternative currently available. Other key features of RCDPeaks include the automatic selection of parameters, the screening of center candidates and the geometrical refining of returned clusters. AVAILABILITY AND IMPLEMENTATION The source code and documentation of RCDPeaks are free and publicly available on GitHub (https://github.com/LQCT/RCDPeaks.git). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Daniel Platero-Rochart
- Departamento de Química-Física, Laboratorio de Química Computacional y Teórica (LQCT), Facultad de Química, Universidad de La Habana, La Habana 10400, Cuba
| | - Roy González-Alemán
- Departamento de Química-Física, Laboratorio de Química Computacional y Teórica (LQCT), Facultad de Química, Universidad de La Habana, La Habana 10400, Cuba.,Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris Saclay, Gif-sur-Yvette F-91198, France
| | - Erix W Hernández-Rodríguez
- Laboratorio de Bioinformática y Química Computacional (LBQC), Facultad de Medicina, Universidad Católica del Maule, Talca 3460000, Chile.,Escuela de Química y Farmacia, Facultad de Medicina, Universidad Católica del Maule, Talca 3460000, Chile
| | - Fabrice Leclerc
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris Saclay, Gif-sur-Yvette F-91198, France
| | - Julio Caballero
- Departamento de Bioinformática, Facultad de Ingeniería, Centro de Bioinformática, Simulación y Modelado (CBSM), Universidad de Talca, Talca 3460000, Chile
| | - Luis Montero-Cabrera
- Departamento de Química-Física, Laboratorio de Química Computacional y Teórica (LQCT), Facultad de Química, Universidad de La Habana, La Habana 10400, Cuba
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Gao T, Chen D, Tang Y, Du B, Ranjan R, Zomaya AY, Dustdar S. Adaptive density peaks clustering: Towards exploratory EEG analysis. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108123] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Ma J, Hao Z, Sun W. Enhancing sparrow search algorithm via multi-strategies for continuous optimization problems. Inf Process Manag 2022. [DOI: 10.1016/j.ipm.2021.102854] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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Sun L, Qin X, Ding W, Xu J. Nearest neighbors-based adaptive density peaks clustering with optimized allocation strategy. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2021.12.019] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Zhang R, Song X, Ying S, Ren H, Zhang B, Wang H. CA-CSM: a novel clustering algorithm based on cluster center selection model. Soft comput 2021. [DOI: 10.1007/s00500-021-05835-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Nguyen TT, Nguyen LT, Nguyen A, Yun U, Vo B. A method for efficient clustering of spatial data in network space. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-202806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Spatial clustering is one of the main techniques for spatial data mining and spatial data analysis. However, existing spatial clustering methods primarily focus on points distributed in planar space with the Euclidean distance measurement. Recently, NS-DBSCAN has been developed to perform clustering of spatial point events in Network Space based on a well-known clustering algorithm, named Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The NS-DBSCAN algorithm has efficiently solved the problem of clustering network constrained spatial points. When compared to the NC_DT (Network-Constraint Delaunay Triangulation) clustering algorithm, the NS-DBSCAN algorithm efficiently solves the problem of clustering network constrained spatial points by visualizing the intrinsic clustering structure of spatial data by constructing density ordering charts. However, the main drawback of this algorithm is when the data are processed, objects that are not specifically categorized into types of clusters cannot be removed, which is undeniably a waste of time, particularly when the dataset is large. In an attempt to have this algorithm work with great efficiency, we thus recommend removing edges that are longer than the threshold and eliminating low-density points from the density ordering table when forming clusters and also take other effective techniques into consideration. In this paper, we develop a theorem to determine the maximum length of an edge in a road segment. Based on this theorem, an algorithm is proposed to greatly improve the performance of the density-based clustering algorithm in network space (NS-DBSCAN). Experiments using our proposed algorithm carried out in collaboration with Ho Chi Minh City, Vietnam yield the same results but shows an advantage of it over NS-DBSCAN in execution time.
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Affiliation(s)
- Trang T.D. Nguyen
- Faculty of Information Technology, Nha Trang University, Nha Trang, Vietnam
| | - Loan T.T. Nguyen
- School of Computer Science and Engineering, International University, Ho Chi Minh City, Vietnam
- Vietnam National University, Ho Chi Minh City, Vietnam
| | - Anh Nguyen
- Department of Applied Informatics, Wroclaw University of Science and Technology, Poland
| | - Unil Yun
- Department of Computer Engineering, Sejong University, Seoul, Republic of Korea
| | - Bay Vo
- Faculty of Information Technology, Ho Chi Minh City University of Technology (HUTECH), Ho Chi Minh City, Vietnam
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