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A State of Art Analysis of Telecommunication Data by k-Means and k-Medoids Clustering Algorithms 认领 引用
1
作者 T. Velmurugan 《Journal of Computer and Communications》 2018年第1期190-202,共13页
Cluster analysis is one of the major data analysis methods widely used for many practical applications in emerging areas of data mining. A good clustering method will produce high quality clusters with high intra-clus... Cluster analysis is one of the major data analysis methods widely used for many practical applications in emerging areas of data mining. A good clustering method will produce high quality clusters with high intra-cluster similarity and low inter-cluster similarity. Clustering techniques are applied in different domains to predict future trends of available data and its uses for the real world. This research work is carried out to find the performance of two of the most delegated, partition based clustering algorithms namely k-Means and k-Medoids. A state of art analysis of these two algorithms is implemented and performance is analyzed based on their clustering result quality by means of its execution time and other components. Telecommunication data is the source data for this analysis. The connection oriented broadband data is given as input to find the clustering quality of the algorithms. Distance between the server locations and their connection is considered for clustering. Execution time for each algorithm is analyzed and the results are compared with one another. Results found in comparison study are satisfactory for the chosen application. 展开更多
关键词 k-Means Algorithm k-Medoids Algorithm Data Clustering Time Complexity Telecommunication Data
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A Quantum-Inspired Algorithm for Clustering and Intrusion Detection 认领 引用
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作者 Gang Xu Lefeng Wang +5 位作者 Yuwei Huang Yong Lu Xin Liu Weijie Tan Zongpeng Li Xiu-Bo Chen 《Computers, Materials & Continua》 SCIE EI 2026年第4期1180-1215,共36页
The Intrusion Detection System(IDS)is a security mechanism developed to observe network traffic and recognize suspicious or malicious activities.Clustering algorithms are often incorporated into IDS;however,convention... The Intrusion Detection System(IDS)is a security mechanism developed to observe network traffic and recognize suspicious or malicious activities.Clustering algorithms are often incorporated into IDS;however,conventional clustering-based methods face notable drawbacks,including poor scalability in handling high-dimensional datasets and a strong dependence of outcomes on initial conditions.To overcome the performance limitations of existing methods,this study proposes a novel quantum-inspired clustering algorithm that relies on a similarity coefficient-based quantum genetic algorithm(SC-QGA)and an improved quantum artificial bee colony algorithm hybrid K-means(IQABC-K).First,the SC-QGA algorithmis constructed based on quantum computing and integrates similarity coefficient theory to strengthen genetic diversity and feature extraction capabilities.For the subsequent clustering phase,the process based on the IQABC-K algorithm is enhanced with the core improvement of adaptive rotation gate and movement exploitation strategies to balance the exploration capabilities of global search and the exploitation capabilities of local search.Simultaneously,the acceleration of convergence toward the global optimum and a reduction in computational complexity are facilitated by means of the global optimum bootstrap strategy and a linear population reduction strategy.Through experimental evaluation with multiple algorithms and diverse performance metrics,the proposed algorithm confirms reliable accuracy on three datasets:KDD CUP99,NSL_KDD,and UNSW_NB15,achieving accuracy of 98.57%,98.81%,and 98.32%,respectively.These results affirm its potential as an effective solution for practical clustering applications. 展开更多
关键词 Intrusion detection clustering quantum artificial bee colony algorithm K-means quantum genetic algorithm
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Equivalent Modeling with Passive Filter Parameter Clustering for Photovoltaic Power Stations Based on a Particle Swarm Optimization K-Means Algorithm 认领 引用
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作者 Binjiang Hu Yihua Zhu +3 位作者 Liang Tu Zun Ma Xian Meng Kewei Xu 《Energy Engineering》 EI 2026年第1期431-459,共29页
This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the compl... This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the complexities,simulation time cost and convergence problems of detailed PV power station models.First,the amplitude–frequency curves of different filter parameters are analyzed.Based on the results,a grouping parameter set for characterizing the external filter characteristics is established.These parameters are further defined as clustering parameters.A single PV inverter model is then established as a prerequisite foundation.The proposed equivalent method combines the global search capability of PSO with the rapid convergence of KMC,effectively overcoming the tendency of KMC to become trapped in local optima.This approach enhances both clustering accuracy and numerical stability when determining equivalence for PV inverter units.Using the proposed clustering method,both a detailed PV power station model and an equivalent model are developed and compared.Simulation and hardwarein-loop(HIL)results based on the equivalent model verify that the equivalent method accurately represents the dynamic characteristics of PVpower stations and adapts well to different operating conditions.The proposed equivalent modeling method provides an effective analysis tool for future renewable energy integration research. 展开更多
关键词 Photovoltaic power station multi-machine equivalentmodeling particle swarmoptimization K-means clustering algorithm
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Enhancing ITS Reliability and Efficiency through Optimal VANET Clustering Using Grasshopper Optimization Algorithm 认领 引用 被引量：2
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作者 Seongsoo Cho Yeonwoo Lee Cheolhee Yoon 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第6期3769-3793,共25页
As vehicular networks grow increasingly complex due to high node mobility and dynamic traffic conditions,efficient clustering mechanisms are vital to ensure stable and scalable communication.Recent studies have emphas... As vehicular networks grow increasingly complex due to high node mobility and dynamic traffic conditions,efficient clustering mechanisms are vital to ensure stable and scalable communication.Recent studies have emphasized the need for adaptive clustering strategies to improve performance in Intelligent Transportation Systems(ITS).This paper presents the Grasshopper Optimization Algorithm for Vehicular Network Clustering(GOAVNET)algorithm,an innovative approach to optimal vehicular clustering in Vehicular Ad-Hoc Networks(VANETs),leveraging the Grasshopper Optimization Algorithm(GOA)to address the critical challenges of traffic congestion and communication inefficiencies in Intelligent Transportation Systems(ITS).The proposed GOA-VNET employs an iterative and interactive optimization mechanism to dynamically adjust node positions and cluster configurations,ensuring robust adaptability to varying vehicular densities and transmission ranges.Key features of GOA-VNET include the utilization of attraction zone,repulsion zone,and comfort zone parameters,which collectively enhance clustering efficiency and minimize congestion within Regions of Interest(ROI).By managing cluster configurations and node densities effectively,GOA-VNET ensures balanced load distribution and seamless data transmission,even in scenarios with high vehicular densities and varying transmission ranges.Comparative evaluations against the Whale Optimization Algorithm(WOA)and Grey Wolf Optimization(GWO)demonstrate that GOA-VNET consistently outperforms these methods by achieving superior clustering efficiency,reducing the number of clusters by up to 10%in high-density scenarios,and improving data transmission reliability.Simulation results reveal that under a 100-600 m transmission range,GOA-VNET achieves an average reduction of 8%-15%in the number of clusters and maintains a 5%-10%improvement in packet delivery ratio(PDR)compared to baseline algorithms.Additionally,the algorithm incorporates a heat transfer-inspired load-balancing mechanism,ensuring equitable distribution of nodes among cluster leaders(CLs)and maintaining a stable network environment.These results validate GOA-VNET as a reliable and scalable solution for VANETs,with significant potential to support next-generation ITS.Future research could further enhance the algorithm by integrating multi-objective optimization techniques and exploring broader applications in complex traffic scenarios. 展开更多
关键词 Grasshopper optimization algorithm VANET intelligent transportation systems traffic congestion clustering efficiency
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Rock discontinuity extraction from 3D point clouds using pointwise clustering algorithm 认领 引用 被引量：1
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作者 Xiaoyu Yi Wenxuan Wu +2 位作者 Wenkai Feng Yongjian Zhou Jiachen Zhao 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2025年第7期4429-4444,共16页
Recognizing discontinuities within rock masses is a critical aspect of rock engineering.The development of remote sensing technologies has significantly enhanced the quality and quantity of the point clouds collected ... Recognizing discontinuities within rock masses is a critical aspect of rock engineering.The development of remote sensing technologies has significantly enhanced the quality and quantity of the point clouds collected from rock outcrops.In response,we propose a workflow that balances accuracy and efficiency to extract discontinuities from massive point clouds.The proposed method employs voxel filtering to downsample point clouds,constructs a point cloud topology using K-d trees,utilizes principal component analysis to calculate the point cloud normals,and employs the pointwise clustering(PWC)algorithm to extract discontinuities from rock outcrop point clouds.This method provides information on the location and orientation(dip direction and dip angle)of the discontinuities,and the modified whale optimization algorithm(MWOA)is utilized to identify major discontinuity sets and their average orientations.Performance evaluations based on three real cases demonstrate that the proposed method significantly reduces computational time costs without sacrificing accuracy.In particular,the method yields more reasonable extraction results for discontinuities with certain undulations.The presented approach offers a novel tool for efficiently extracting discontinuities from large-scale point clouds. 展开更多
关键词 Rock mass discontinuity 3D point clouds Pointwise clustering(PWC)algorithm Modified whale optimization algorithm(MWOA)
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Phasmatodea Population Evolution Algorithm Based on Spiral Mechanism and Its Application to Data Clustering 认领 引用
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作者 Jeng-Shyang Pan Mengfei Zhang +2 位作者 Shu-Chuan Chu Xingsi Xue Václav Snášel 《Computers, Materials & Continua》 SCIE EI 2025年第4期475-496,共22页
Data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data analysis.Traditional clustering algorithms,such as K-means,are widely used due to their sim... Data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data analysis.Traditional clustering algorithms,such as K-means,are widely used due to their simplicity and efficiency.This paper proposes a novel Spiral Mechanism-Optimized Phasmatodea Population Evolution Algorithm(SPPE)to improve clustering performance.The SPPE algorithm introduces several enhancements to the standard Phasmatodea Population Evolution(PPE)algorithm.Firstly,a Variable Neighborhood Search(VNS)factor is incorporated to strengthen the local search capability and foster population diversity.Secondly,a position update model,incorporating a spiral mechanism,is designed to improve the algorithm’s global exploration and convergence speed.Finally,a dynamic balancing factor,guided by fitness values,adjusts the search process to balance exploration and exploitation effectively.The performance of SPPE is first validated on CEC2013 benchmark functions,where it demonstrates excellent convergence speed and superior optimization results compared to several state-of-the-art metaheuristic algorithms.To further verify its practical applicability,SPPE is combined with the K-means algorithm for data clustering and tested on seven datasets.Experimental results show that SPPE-K-means improves clustering accuracy,reduces dependency on initialization,and outperforms other clustering approaches.This study highlights SPPE’s robustness and efficiency in solving both optimization and clustering challenges,making it a promising tool for complex data analysis tasks. 展开更多
关键词 Phasmatodea population evolution algorithm data clustering meta-heuristic algorithm
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A systematic data-driven modelling framework for nonlinear distillation processes incorporating data intervals clustering and new integrated learning algorithm 认领 引用
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作者 Zhe Wang Renchu He Jian Long 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2025年第5期182-199,共18页
The distillation process is an important chemical process,and the application of data-driven modelling approach has the potential to reduce model complexity compared to mechanistic modelling,thus improving the efficie... The distillation process is an important chemical process,and the application of data-driven modelling approach has the potential to reduce model complexity compared to mechanistic modelling,thus improving the efficiency of process optimization or monitoring studies.However,the distillation process is highly nonlinear and has multiple uncertainty perturbation intervals,which brings challenges to accurate data-driven modelling of distillation processes.This paper proposes a systematic data-driven modelling framework to solve these problems.Firstly,data segment variance was introduced into the K-means algorithm to form K-means data interval(KMDI)clustering in order to cluster the data into perturbed and steady state intervals for steady-state data extraction.Secondly,maximal information coefficient(MIC)was employed to calculate the nonlinear correlation between variables for removing redundant features.Finally,extreme gradient boosting(XGBoost)was integrated as the basic learner into adaptive boosting(AdaBoost)with the error threshold(ET)set to improve weights update strategy to construct the new integrated learning algorithm,XGBoost-AdaBoost-ET.The superiority of the proposed framework is verified by applying this data-driven modelling framework to a real industrial process of propylene distillation. 展开更多
关键词 Integrated learning algorithm Data intervals clustering Feature selection Application of artificial intelligence in distillation industry Data-driven modelling
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An Efficient Clustering Algorithm for Enhancing the Lifetime and Energy Efficiency of Wireless Sensor Networks 认领 引用
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作者 Peng Zhou Wei Chen Bingyu Cao 《Computers, Materials & Continua》 SCIE EI 2025年第9期5337-5360,共24页
Wireless Sensor Networks(WSNs),as a crucial component of the Internet of Things(IoT),are widely used in environmental monitoring,industrial control,and security surveillance.However,WSNs still face challenges such as ... Wireless Sensor Networks(WSNs),as a crucial component of the Internet of Things(IoT),are widely used in environmental monitoring,industrial control,and security surveillance.However,WSNs still face challenges such as inaccurate node clustering,low energy efficiency,and shortened network lifespan in practical deployments,which significantly limit their large-scale application.To address these issues,this paper proposes an Adaptive Chaotic Ant Colony Optimization algorithm(AC-ACO),aiming to optimize the energy utilization and system lifespan of WSNs.AC-ACO combines the path-planning capability of Ant Colony Optimization(ACO)with the dynamic characteristics of chaotic mapping and introduces an adaptive mechanism to enhance the algorithm’s flexibility and adaptability.By dynamically adjusting the pheromone evaporation factor and heuristic weights,efficient node clustering is achieved.Additionally,a chaotic mapping initialization strategy is employed to enhance population diversity and avoid premature convergence.To validate the algorithm’s performance,this paper compares AC-ACO with clustering methods such as Low-Energy Adaptive Clustering Hierarchy(LEACH),ACO,Particle Swarm Optimization(PSO),and Genetic Algorithm(GA).Simulation results demonstrate that AC-ACO outperforms the compared algorithms in key metrics such as energy consumption optimization,network lifetime extension,and communication delay reduction,providing an efficient solution for improving energy efficiency and ensuring long-term stable operation of wireless sensor networks. 展开更多
关键词 Internet of Things wireless sensor networks ant colony optimization clustering algorithm energy efficiency
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A Resilient BIRCH-Based Smart Framework for Real-Time IoT Data Clustering 认领 引用
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作者 Prabhat Das Dibya Jyoti Bora +2 位作者 Sajal Saha Cheng-Chi Lee Hirak Mazumdar 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期864-898,共35页
Real-time data processing is essential in the evolving landscape of IoT applications,ensuring efficiency,reliability,and adaptability.However,conventional clustering algorithms often face difficulties in managing high... Real-time data processing is essential in the evolving landscape of IoT applications,ensuring efficiency,reliability,and adaptability.However,conventional clustering algorithms often face difficulties in managing highfrequency,continuous IoT data streams due to limited adaptability and high computational overhead.To address these challenges,this study proposes a resilient adaptation of the BIRCH(Balanced Iterative Reducing and Clustering using Hierarchies)algorithm,tailored specifically for streaming IoT data.The enhanced approach dynamically recalculates clusters and determines the optimal number of clusters using the KneeLocator method.Unlike the original batchoriented BIRCH,the modified version processes data incrementally,enabling continuous adaptation to changing data distributions.The proposed method was validated on benchmark IoT datasets and compared against K-Means,DBSCAN,standard BIRCH,and other state-of-the-art streaming-based clustering algorithms.Results consistently show that the modified BIRCH outperforms existing approaches in execution speed,memory efficiency,scalability,and clustering accuracy.In addition,the algorithm has been deployed within a web-based application featuring interactive visualization and anomaly detection,highlighting its practical relevance for smart city and industrial IoT scenarios.To promote reproducibility and future research,the complete framework and source code have been made publicly available. 展开更多
关键词 IoT applications clustering algorithms smart city applications real-time data processing industrial IoT real-time clustering
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iPAFAR: An Adaptive Pareto-Based NS-AAA Energy-Stable Fuzzy Clustering and Routing Framework for Smart City IoT-Enabled WSNs 认领 引用
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作者 Bhanu Talwar Puneet Thapar +3 位作者 Tahani Alsubait Mai Alduailij Ateeq Ur Rehman Salil Bharany 《Computers, Materials & Continua》 SCIE EI 2026年第8期785-805,共21页
Wireless Sensor Networks(WSNs)play a vital role in smart city Internet of Things(IoT)applications,including environmental monitoring,intelligent transportation,and infrastructure management.However,limited battery cap... Wireless Sensor Networks(WSNs)play a vital role in smart city Internet of Things(IoT)applications,including environmental monitoring,intelligent transportation,and infrastructure management.However,limited battery capacity,uneven energy consumption,and inefficient clustering and routing mechanisms significantly reduce network lifetime,reliability,and scalability,especially in large-scale IoT deployments.Traditional routing protocols often rely on single-objective optimization or static clustering strategies,which fail to maintain long-term energy balance and stable communication performance.To address these challenges,this paper proposes iPAFAR,a Pareto-based multi-objective clustering and routing framework designed for IoT-enabled WSNs.The proposed model formulates cluster-head selection as a multi-objective optimization problem that considers residual energy,node centrality,load variance,and fairness.A Non-Dominated Sorting Artificial Algae Algorithm(NS-AAA)is used to obtain Pareto-optimal cluster-head configurations,followed by a fuzzy inference system for refined decision-making.To ensure long-term energy stability,a Lyapunov-based routing model is incorporated,and an adaptive re-clustering mechanism is introduced to reduce unnecessary control overhead under dynamic network conditions.The performance of the proposed framework is evaluated through MATLAB-based simulations and compared with existing protocols,including LEACH-M,ME-LEACH,FQA,MKNDPC,RANP-PSO,and BKA-TOA.Experimental results show that iPAFAR achieves approximately 40%lower end-to-end delay,15%–20%higher packet delivery ratio,and 45%–50%improvement in residual energy while maintaining nearly twice the number of active nodes after 1000 simulation rounds.These results confirm that the proposed framework provides improved energy efficiency,load balancing,and routing stability,making it suitable for long-term smart city IoT deployments. 展开更多
关键词 Wireless sensor networks smart cities multi-objective optimization artificial algae algorithm energy-aware clustering pareto optimization IoT routing
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The Bayesian Gaussian mixture model with nearest-neighbor distance(BGMM-NND)algorithm:A new earthquake clustering method and its application to the Sichuan–Yunnan Block 认领 引用 被引量：1
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作者 JieYi Hou Feng Hu +1 位作者 Yang Zang LingYuan Meng 《Earth and Planetary Physics》 EI CSCD 2025年第4期828-841,共14页
We propose a robust earthquake clustering method:the Bayesian Gaussian mixture model with nearest-neighbor distance(BGMM-NND)algorithm.Unlike the conventional nearest neighbor distance method,the BGMM-NND algorithm el... We propose a robust earthquake clustering method:the Bayesian Gaussian mixture model with nearest-neighbor distance(BGMM-NND)algorithm.Unlike the conventional nearest neighbor distance method,the BGMM-NND algorithm eliminates the need for hyperparameter tuning or reliance on fixed thresholds,offering enhanced flexibility for clustering across varied seismic scales.By integrating cumulative probability and BGMM with principal component analysis(PCA),the BGMM-NND algorithm effectively distinguishes between background and triggered earthquakes while maintaining the magnitude component and resolving the issue of excessively large spatial cluster domains.We apply the BGMM-NND algorithm to the Sichuan–Yunnan seismic catalog from 1971 to 2024,revealing notable variations in earthquake frequency,triggering characteristics,and recurrence patterns across different fault zones.Distinct clustering and triggering behaviors are identified along different segments of the Longmenshan Fault.Multiple seismic modes,namely,the short-distance mode,the medium-distance mode,the repeating-like mode,the uniform background mode,and the Wenchuan mode,are uncovered.The algorithm's flexibility and robust performance in earthquake clustering makes it a valuable tool for exploring seismicity characteristics,offering new insights into earthquake clustering and the spatiotemporal patterns of seismic activity. 展开更多
关键词 earthquake clustering BGMM-NND algorithm Sichuan–Yunnan Block seismic modes
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Clustering-based recommendation method with enhanced grasshopper optimisation algorithm 认领 引用 被引量：1
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作者 Zihao Zhao Yingchun Xia +7 位作者 Wenjun Xu Hui Yu Shuai Yang Cheng Chen Xiaohui Yuan Xiaobo Zhou Qingyong Wang Lichuan Gu 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2025年第2期494-509,共16页
In the era of big data,personalised recommendation systems are essential for enhancing user engagement and driving business growth.However,traditional recommendation algorithms,such as collaborative filtering,face sig... In the era of big data,personalised recommendation systems are essential for enhancing user engagement and driving business growth.However,traditional recommendation algorithms,such as collaborative filtering,face significant challenges due to data sparsity,algorithm scalability,and the difficulty of adapting to dynamic user preferences.These limitations hinder the ability of systems to provide highly accurate and personalised recommendations.To address these challenges,this paper proposes a clustering-based recommendation method that integrates an enhanced Grasshopper Optimisation Algorithm(GOA),termed LCGOA,to improve the accuracy and efficiency of recommendation systems by optimising cluster centroids in a dynamic environment.By combining the K-means algorithm with the enhanced GOA,which incorporates a Lévy flight mechanism and multi-strategy co-evolution,our method overcomes the centroid sensitivity issue,a key limitation in traditional clustering techniques.Experimental results across multiple datasets show that the proposed LCGOA-based method significantly outperforms conventional recommendation algorithms in terms of recommendation accuracy,offering more relevant content to users and driving greater customer satisfaction and business growth. 展开更多
关键词 collaborative recommendation Grasshopper Optimization Algorithm(GOA) K‐means clustering Lévy flight
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基于K-medoids聚类的分布式光伏集群方法及短期功率预测 认领 引用
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作者 蒋亚雪 钱晶 +3 位作者 何昊城 张皓彦 曹雷 毛西蒙 《太阳能学报》 EI CAS CSCD 北大核心 2026年第6期645-655,共11页
为实现对光伏集群的科学聚类划分,该文融合考虑气象因素和地理位置对光伏电站聚类的影响,将出力特性作为子集群的划分特征,提出一种基于改进曼哈顿距离的K中心点聚类算法(K-中心点)。光伏集群聚类的两个关键是气象特征的有效识别和各电... 为实现对光伏集群的科学聚类划分,该文融合考虑气象因素和地理位置对光伏电站聚类的影响,将出力特性作为子集群的划分特征,提出一种基于改进曼哈顿距离的K中心点聚类算法(K-中心点)。光伏集群聚类的两个关键是气象特征的有效识别和各电站的相似性度量,为此采用变异系数法与秩和比法相结合对气象输入特征进行提取,可提高特征的识别有效性。另一方面将改进曼哈顿距离引入K-中心点聚类算法中,充分挖掘数据的动态特性,并采用3种内部有效指标优化聚类划分数,避免常规划分带来的分散性和偏差,提高聚类的整体效果。此外,在聚类基础上进行预测验证,为解决预测模型的参数设置问题,在预测模型中融入优化算法,采用改进灰狼优化算法(IGWO)对iTransformer和极度梯度提升(XGBoost)模型中的参数进行优化,功率预测结果表明所提聚类算法的有效性以及较高的精度。 展开更多
关键词 光伏发电 聚类分析 功率预测 改进灰狼优化算法 iTransformer 极度梯度提升
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Energy Efficient Clustering and Sink Mobility Protocol Using Hybrid Golden Jackal and Improved Whale Optimization Algorithm for Improving Network Longevity in WSNs 认领 引用
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作者 S B Lenin R Sugumar +2 位作者 J S Adeline Johnsana N Tamilarasan R Nathiya 《China Communications》 SCIE EI CSCD 2025年第3期16-35,共20页
Reliable Cluster Head(CH)selectionbased routing protocols are necessary for increasing the packet transmission efficiency with optimal path discovery that never introduces degradation over the transmission reliability... Reliable Cluster Head(CH)selectionbased routing protocols are necessary for increasing the packet transmission efficiency with optimal path discovery that never introduces degradation over the transmission reliability.In this paper,Hybrid Golden Jackal,and Improved Whale Optimization Algorithm(HGJIWOA)is proposed as an effective and optimal routing protocol that guarantees efficient routing of data packets in the established between the CHs and the movable sink.This HGJIWOA included the phases of Dynamic Lens-Imaging Learning Strategy and Novel Update Rules for determining the reliable route essential for data packets broadcasting attained through fitness measure estimation-based CH selection.The process of CH selection achieved using Golden Jackal Optimization Algorithm(GJOA)completely depends on the factors of maintainability,consistency,trust,delay,and energy.The adopted GJOA algorithm play a dominant role in determining the optimal path of routing depending on the parameter of reduced delay and minimal distance.It further utilized Improved Whale Optimisation Algorithm(IWOA)for forwarding the data from chosen CHs to the BS via optimized route depending on the parameters of energy and distance.It also included a reliable route maintenance process that aids in deciding the selected route through which data need to be transmitted or re-routed.The simulation outcomes of the proposed HGJIWOA mechanism with different sensor nodes confirmed an improved mean throughput of 18.21%,sustained residual energy of 19.64%with minimized end-to-end delay of 21.82%,better than the competitive CH selection approaches. 展开更多
关键词 Cluster Heads(CHs) Golden Jackal Optimization Algorithm(GJOA) Improved Whale Optimization Algorithm(IWOA) unequal clustering
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A Clustering Model Based on Density Peak Clustering and the Sparrow Search Algorithm for VANETs 认领 引用
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作者 Chaoliang Wang Qi Fu Zhaohui Li 《Computers, Materials & Continua》 SCIE EI 2025年第8期3707-3729,共23页
Cluster-basedmodels have numerous application scenarios in vehicular ad-hoc networks(VANETs)and can greatly help improve the communication performance of VANETs.However,the frequent movement of vehicles can often lead... Cluster-basedmodels have numerous application scenarios in vehicular ad-hoc networks(VANETs)and can greatly help improve the communication performance of VANETs.However,the frequent movement of vehicles can often lead to changes in the network topology,thereby reducing cluster stability in urban scenarios.To address this issue,we propose a clustering model based on the density peak clustering(DPC)method and sparrow search algorithm(SSA),named SDPC.First,the model constructs a fitness function based on the parameters obtained from the DPC method and deploys the SSA for iterative optimization to select cluster heads(CHs).Then,the vehicles that have not been selected as CHs are assigned to appropriate clusters by comprehensively considering the distance parameter and link-reliability parameter.Finally,cluster maintenance strategies are considered to tackle the changes in the clusters’organizational structure.To verify the performance of the model,we conducted a simulation on a real-world scenario for multiple metrics related to clusters’stability.The results show that compared with the APROVE and the GAPC,SDPC showed clear performance advantages,indicating that SDPC can effectively ensure VANETs’cluster stability in urban scenarios. 展开更多
关键词 VANETs cluster density peak clustering sparrow search algorithm
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Flight Trajectory Option Set Generation Based on Clustering Algorithms 认领 引用
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作者 WANG Shijin SUN Min +1 位作者 LI Yinglin YANG Baotian 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2025年第6期767-788,共22页
Addressing the issue that flight plans between Chinese city pairs typically rely on a single route,lacking alternative paths and posing challenges in responding to emergencies,this study employs the“quantile-inflecti... Addressing the issue that flight plans between Chinese city pairs typically rely on a single route,lacking alternative paths and posing challenges in responding to emergencies,this study employs the“quantile-inflection point method”to analyze specific deviation trajectories,determine deviation thresholds,and identify commonly used deviation paths.By combining multiple similarity metrics,including Euclidean distance,Hausdorff distance,and sector edit distance,with the density-based spatial clustering of applications with noise(DBSCAN)algorithm,the study clusters deviation trajectories to construct a multi-option trajectory set for city pairs.A case study of 23578 flight trajectories between the Guangzhou airport cluster and the Shanghai airport cluster demonstrates the effectiveness of the proposed framework.Experimental results show that sector edit distance achieves superior clustering performance compared to Euclidean and Hausdorff distances,with higher silhouette coefficients and lower Davies⁃Bouldin indices,ensuring better intra-cluster compactness and inter-cluster separation.Based on clustering results,19 representative trajectory options are identified,covering both nominal and deviation paths,which significantly enhance route diversity and reflect actual flight practices.This provides a practical basis for optimizing flight paths and scheduling,enhancing the flexibility of route selection for flights between city pairs. 展开更多
关键词 flight trajectory clustering trajectory option set sector edit distance density-based spatial clustering of applications with noise(DBSCAN)algorithm deviation trajectories
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Modified tensile-shear statistical damage constitutive model based on AE clustering for rock and concrete materials under thermal-mechanical loading 认领 引用
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作者 Kai SHEN Jia-peng LIU +3 位作者 Qiu-hua RAO Ze-lin LIU Shao-bo JIN Peng LIU 《Transactions of Nonferrous Metals Society of China》 SCIE EI CAS CSCD 2026年第8期2424-2442,共19页
Uniaxial compression tests combined with acoustic emission(AE)monitoring were conducted on the sandstone and concrete specimens to measure the physical and mechanical properties and analyze the damage evolution after ... Uniaxial compression tests combined with acoustic emission(AE)monitoring were conducted on the sandstone and concrete specimens to measure the physical and mechanical properties and analyze the damage evolution after heating test.A modified thermal-mechanical statistical damage constitutive model was established by K-means++clustering algorithm for determining the proportion of tensile and shear cracks in AE data and for defining the new tensile and shear mechanical damage factors of the Weibull distribution.This model is verified to be valid by good agreement with the experimental results. 展开更多
关键词 thermal-mechanical damage statistical damage constitutive model clustering algorithm acoustic emission rock concrete
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融合K-medoids聚类算法的多维数据处理技术 认领 引用 被引量：1
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作者 郭宏文 李晖 +2 位作者 郑灶贤 刘浩 刘鑫 《信息技术》 2026年第3期194-198,共5页
针对传统算法在处理各类电网数据中存在异常数据识别准确度低以及校核能力差等问题,文中设计了一种融合K-medoids聚类和多维数据处理技术的改进算法。通过K-medoids聚类算法对数据进行分析,并划分为不同的簇,在每个簇内利用多维数据处... 针对传统算法在处理各类电网数据中存在异常数据识别准确度低以及校核能力差等问题,文中设计了一种融合K-medoids聚类和多维数据处理技术的改进算法。通过K-medoids聚类算法对数据进行分析,并划分为不同的簇,在每个簇内利用多维数据处理技术对数据特征进行校核,然后再对异常数据进行识别。利用多维数据之间的关系自主检查数据的一致性和准确性,对数据异常值进行分析与标记,提高了对异常数据的识别能力。在MATLAB中以电力财务数据为样本对改进算法进行验证,结果显示所提算法的异常数据识别准确率稳定在95%以上,识别效率为96.5%。 展开更多
关键词 K-medoids聚类算法 多维数据处理 电力数据 模糊数据处理算法
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Method of Modulation Recognition Based on Combination Algorithm of K-Means Clustering and Grading Training SVM 认领 引用 被引量：14
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作者 Faquan Yang Ling Yang +3 位作者 Dong Wang Peihan Qi Haiyan Wang 无 《China Communications》 SCIE CSCD 2018年第12期55-63,共9页
For the existing support vector machine,when recognizing more questions,the shortcomings of high computational complexity and low recognition rate under the low SNR are emerged.The characteristic parameter of the sign... For the existing support vector machine,when recognizing more questions,the shortcomings of high computational complexity and low recognition rate under the low SNR are emerged.The characteristic parameter of the signal is extracted and optimized by using a clustering algorithm,support vector machine is trained by grading algorithm so as to enhance the rate of convergence,improve the performance of recognition under the low SNR and realize modulation recognition of the signal based on the modulation system of the constellation diagram in this paper.Simulation results show that the average recognition rate based on this algorithm is enhanced over 30%compared with methods that adopting clustering algorithm or support vector machine respectively under the low SNR.The average recognition rate can reach 90%when the SNR is 5 dB,and the method is easy to be achieved so that it has broad application prospect in the modulating recognition. 展开更多
关键词 clustering algorithm feature extraction grading algorithm support vector machine modulation recognition
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Improved k-means clustering algorithm 认领 引用 被引量：26
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作者 夏士雄 李文超 +2 位作者 周勇 张磊 牛强 《Journal of Southeast University(English Edition)》 EI CAS 2007年第3期435-438,共4页
In allusion to the disadvantage of having to obtain the number of clusters of data sets in advance and the sensitivity to selecting initial clustering centers in the k-means algorithm, an improved k-means clustering a... In allusion to the disadvantage of having to obtain the number of clusters of data sets in advance and the sensitivity to selecting initial clustering centers in the k-means algorithm, an improved k-means clustering algorithm is proposed. First, the concept of a silhouette coefficient is introduced, and the optimal clustering number Kopt of a data set with unknown class information is confirmed by calculating the silhouette coefficient of objects in clusters under different K values. Then the distribution of the data set is obtained through hierarchical clustering and the initial clustering-centers are confirmed. Finally, the clustering is completed by the traditional k-means clustering. By the theoretical analysis, it is proved that the improved k-means clustering algorithm has proper computational complexity. The experimental results of IRIS testing data set show that the algorithm can distinguish different clusters reasonably and recognize the outliers efficiently, and the entropy generated by the algorithm is lower. 展开更多
关键词 clustering k-means algorithm silhouette coefficient
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