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Integrating Fuzzy clustering and Graph Convolution Network to Accurately Identify Clusters From Attributed Graph

IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2025年第2期12卷 1112-1125页

作者： Yang, Yue Li, Guodong Li, Dongxu Zhang, Jun Hu, Pengwei Hu, Lun Chinese Acad Sci Xinjiang Tech Inst Phys & Chem Urumqi 830000 Peoples R China

Attributed graph clustering is of significance for an in-depth understanding of the intrinsic organization of complex networks. Recently, owing to the powerful learning capability of deep neural networks, increasing attention has been paid to developing more advanced deep learning approaches for attributed graph clustering. However, these approaches mainly focus on the purpose of obtaining general node representations, followed by clustering as a downstream task in combination with traditional techniques. Intuitively, the clustering performance is further limited due to the lack of achieving the desirable clustering quality in the graph representation learning process. To this end, this paper proposes a novel end-to-end attributed graph clustering model, namely FCGCN, by integrating fuzzy clustering and graph convolution network. FCGCN is trained toward optimizing an unsupervised fuzzy-based clustering objective, which is specifically formulated for precisely evaluating the clustering quality by considering the fuzzy memberships of nodes over clusters. To avoid the generation of undesirable clusters, we introduce a tailored regularization term by alleviating the over-smoothing issue on graph neural networks. By doing so, an explicit connection between graph representation learning and clustering can thus be established, considerably improving the clustering performance. To evaluate the performance of FCGCN, extensive experiments are conducted on a total of five real attributed graph datasets, and the results demonstrate the superiority of FCGCN over several state-of-the-art algorithms in terms of effectiveness and efficiency.

关键词： clustering algorithms Representation learning Graph neural networks Optimization Convolution clustering methods Feature extraction Deep learning Loss measurement Data mining Attributed graph fuzzy clustering graph representation graph neural network

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Hierarchical clustering-Based Algorithm for Deployment Planning of LoRa Gateway

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IEEE ACCESS 2025年 13卷 25837-25857页

作者： Lutfie, Kalam Adhiansyah Purnamasari, Prima Dewi Gunawan, Dadang Enriko, I. Ketut Agung Juwono, Filbert H. Univ Indonesia Dept Elect Engn Depok City 16424 Indonesia Inst Teknol Telkom Purwokerto Dept Telecommun & Elect Engn Purwokerto Indonesia Xian Jiaotong Liverpool Univ Dept Elect & Elect Engn Suzhou 215123 Peoples R China

Long-Range Wide Area Network (LoRaWAN) has become a promising communication method for the Internet of Things (IoT) system since it is capable of long-range communication with low power usage. Presently, LoRa gateway (GW) deployment in Indonesia relies on conventional methods, i.e., predicting coverage without incorporating formulas specific to LoRa GW performance or accounting for the area type. This research aims to deploy the LoRa GW using a machine-learning algorithm to cover every sensor demand. In this paper, we simulate, deploy, and measure the signal strength transmission between recommended LoRa GWs and end device (ED) demands to ensure the algorithm works and is usable on each different environment characteristic. The results of these experiments show that the algorithm can generate coordinate point recommendations for the LoRa GW to cover all ED demands in three different characteristic areas.

关键词： LoRaWAN Logic gates Internet of Things clustering algorithms Couplings Wireless communication Actuators Wireless sensor networks Sensitivity Propagation losses LoRa machine learning IoT

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Robust Distributed clustering With Redundant Data Assignment

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IEEE TRANSACTIONS ON INFORMATION THEORY 2025年第4期71卷 2888-2908页

作者： Bulusu, Saikiran Gandikota, Venkata Mazumdar, Arya Rawat, Ankit Singh Varshney, Pramod K. Ohio State Univ Dept Elect & Comp Engn Columbus OH 43210 USA Syracuse Univ Elect Engn & Comp Sci Dept Syracuse NY 13202 USA Univ Calif San Diego Halioglu Data Sci Inst HDSI San Diego CA 92093 USA Google Res NY New York NY 10011 USA

In this work, we present distributed clustering algorithms that can handle large-scale data across multiple machines in the presence of faulty machines. These faulty machines can either be straggling machines that fail to respond within a stipulated time or Byzantines that send arbitrary responses. We propose redundant data assignment schemes that enable us to obtain clustering solutions based on the entire dataset, even when some machines are stragglers or adversarial in nature. Our proposed robust clustering algorithms generate a constant factor approximate solution in the presence of stragglers or Byzantines. We also provide various constructions of the data assignment scheme that provide resilience against a large fraction of faulty machines. Simulation results show that the distributed algorithms based on the proposed assignment scheme provide good-quality solutions for a variety of clustering problems.

关键词： clustering algorithms Costs Distributed databases Resilience Cost function Delays Computational modeling Computational efficiency Approximation algorithms Simulation Distributed clustering coded computations Byzantine-resilient straggler-resilient

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A Radar Signal Deinterleaving Method Based on Enhanced Sparse Subspace clustering

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2025年第2期61卷 2956-2972页

作者： Guo, Qiang Huang, Shuai Liu, Li-Chao Kaliuzhnyi, Mykola Douplii, Stepan Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Peoples R China Kharkiv Natl Univ Radio Elect Adv Res Lab UA-61166 Kharkov Ukraine Univ Munster Ctr Informat Technol D-48149 Munster Germany

In the modern information battlefield, radar signal deinterleaving plays a pivotal role in perceiving the electromagnetic spatial situation and grasping the initiative of the battle. The increasingly complex electromagnetic environment of modern battlefields leads to serious feature distribution space overlap of radar signal parameters, resulting in limited intraclass aggregation and interclass separability. Therefore, this article introduces the concept of a sparse subspace and proposes a radar signal deinterleaving method based on enhanced sparse subspace clustering. The generalized orthogonal matching pursuit algorithm is used in this study to construct a complex network on the basis of the self-expressiveness property among radar pulses in the same subspace, reducing the connection of pulses from different radars and thus decreasing computational complexity. The graph segmentation concept is incorporated into this algorithm, and Laplacian spectrum decomposition is employed to mine the geometric correlation from radar pulse signals. This approach enables efficient radar signal deinterleaving under nonideal conditions. Simultaneously, an algorithm based on an energy-corrected splitting threshold is proposed to calculate the number of cluster centers accurately. The proposed method effectively addresses the deinterleaving error issue caused by overlapping or missing working state parameters in intercepted complex radar pulse signals, achieving a deinterleaving accuracy of 94.74% for ten interleaved radar pulse signals with severe parameter feature overlap.

关键词： Radar clustering algorithms Matching pursuit algorithms Complex networks Approximation algorithms Electromagnetics Radar countermeasures Computational complexity Accuracy Spaceborne radar Complex network graph segmentation parameter feature overlap radar signal deinterleaving sparse subspace clustering

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clustering Algorithm by Boundary Detection Base on Entropy of KNN

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IEEE ACCESS 2025年 13卷 62353-62366页

作者： Ding, Jiaman Yin, Jinyuan Jia, Lianyin Fu, Xiaodong Wang, Hongbin Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming 650500 Peoples R China Kunming Univ Sci & Technol Artificial Intelligence Key Lab Yunnan Prov Kunming 650500 Peoples R China

clustering analysis has been widely applied in various fields, and boundary detection based clustering algorithms have shown effective performance. In this work, we propose a clustering algorithm by boundary detection based on entropy of KNN (CBDEK). A border point contains only the nearest neighbors within a specific directional range. Thus, we define entropy of KNN (EK) to accurately identify the boundary of the cluster. Since entropy has the property of measuring uncertainty, it can be used to quantify the possibility that a point is a border point. A lower EK indicates an uneven neighbor distribution, increasing the possibility of the point being a border point. Then, the border points are clustered based on the directional similarity of their nearest neighbors. Specifically, if a border point is a neighbor of another border point and most of their nearest neighbors show directional similarity, they are considered to belong to the same cluster. Furthermore, we assign the label of a border point to the interior points located within the maximum nearest neighbor sub-block of this border point to facilitate an efficient allocation for the remaining points (interior points). In addition, our algorithm incorporates noise mitigation techniques using average distance and box plot analysis. The effectiveness of CBDEK is proven by a comparative evaluation of nine algorithms on 24 datasets.

关键词： clustering algorithms Nearest neighbor methods Entropy Noise Object recognition Shape Vectors Prevention and mitigation Noise measurement Uncertainty Boundary detection box plot clustering directional similarity entropy of KNN

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Time-Domain-clustering Based Dynamic Channel Emulation for Millimeter-Wave MIMO Over-the-Air Testing

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2025年第4期73卷 2086-2096页

作者： Jiang, Zhengbo Zhao, Wentao Guo, Chong Yan, Mengjie Hao, Zhang-Cheng Hong, Wei Southeast Univ Sch Informat Sci & Engn State Key Lab Millimeter Waves Nanjing 210096 Peoples R China Nanjing Meatron Elect Co Ltd Nanjing 210096 Peoples R China

This article presents a novel methodology to emulate dynamic channels within the multi-probe anechoic chamber (MPAC) framework for over-the-air (OTA) testing. The approach integrates time-domain clustering and multi-objective optimization algorithms to emulate dynamic channels precisely. Simulations and experimental validation have been provided to demonstrate the effectiveness of the proposed method, where power-angular-spectrum (PAS) similarity percentage (PSP) values surpassing 87% can be consistently achieved. The time-domain clustering and probe selection technique significantly contribute to the reduction of emulation complexity by compressing both temporal and spatial domains of the channel. Consequently, this strategy enhances the feasibility of system performance testing in dynamic propagation scenarios within the MPAC and offers a novel framework for the emulation of dynamic channels in MPAC OTA testing.

关键词： Probes Testing Emulation Millimeter wave communication clustering algorithms Time-domain analysis Heuristic algorithms Channel emulation clustering dynamic channel multi-probe anechoic chamber (MPAC) over-the-air (OTA) testing

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Characterization and clustering of rock discontinuity sets:A review

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Journal of Rock Mechanics and Geotechnical Engineering 2025年第2期17卷 1240-1262页

作者： Changle Pu Jiewei Zhan Wen Zhang Jianbing Peng College of Geological Engineering and Geomatics Chang'an UniversityXi'an710054China Key Laboratory of Western China's Mineral Resources and Geological Engineering Ministry of EducationChang'an UniversityXi'an710054China College of Construction Engineering Jilin UniversityChangchun130026China

The characterization and clustering of rock discontinuity sets are a crucial and challenging task in rock mechanics and geotechnical *** the past few decades,the clustering of discontinuity sets has undergone rapid and remarkable ***,there is no relevant literature summarizing these achievements,and this paper attempts to elaborate on the current status and prospects in this ***,this review aims to discuss the development process of clustering methods for discontinuity sets and the state-of-the-art relevant ***,we introduce the importance of discontinuity clustering analysis and follow the comprehensive characterization approaches of discontinuity data.A bibliometric analysis is subsequently conducted to clarify the current status and development characteristics of the clustering of discontinuity *** methods for the clustering analysis of rock discontinuities are reviewed in terms of single-and multi-parameter clustering ***-parameter methods can be classified into empirical judgment methods,dynamic clustering methods,relative static clustering methods,and static clustering methods,reflecting the continuous optimization and improvement of clustering ***,this paper compares the current mainstream of single-parameter clustering methods with multi-parameter clustering *** is emphasized that the current single-parameter clustering methods have reached their performance limits,with little room for improvement,and that there is a need to extend the study of multi-parameter clustering ***,several suggestions are offered for future research on the clustering of discontinuity sets.

关键词： Discontinuity clustering clustering algorithms Discontinuity characterization Orientation analysis Rock mass

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Automatic Point Cloud clustering for Surface Defect Diagnosis

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 12538-12547页

作者： Ye, Jidong Liu, Xingjian Madhusudanan, Harikrishnan Wang, Yue Zhu, Junhui Wang, Yong Ru, Changhai Liu, Xinyu Sun, Yu Dalian Univ Technol Inst Robot & Intelligent Syst IRIS Dalian 116024 Peoples R China Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China Dalian Univ Technol State Key Lab High Performance Precis Mfg Dalian 116024 Peoples R China Univ Toronto Dept Mech & Ind Engn Toronto ON M5S 3G8 Canada Suzhou Univ Sci & Technol Sch Elect & Informat Engn Suzhou 215009 Peoples R China

Point cloud clustering is a promising method for 3D surface defect diagnosis in manufacturing but requires manual clustering parameter selection, reducing usability. This paper proposes an automatic point cloud clustering method to address this issue. It employs a strategy that progresses from coarse to fine. In the coarse searching stage, a K-Nearest Neighbor (KNN) graph analysis technique is developed to recognize potential defective regions in parallel. Moving on to the fine stage of extracting detailed defects, a modified DBSCAN algorithm is proposed, in which the clustering parameters are calculated automatically from the KNN graph analysis results. Experimental results showed that the proposed method achieved cloud clustering with automatically calculated clustering parameters for surface defect diagnosis. The proposed method outperformed the traditional region growing algorithm in accuracy (0.942 vs. 0.680) and processing speed (21500 points/sec vs. 8740 points/sec) without requiring manual intervention.

关键词： Point cloud compression Nearest neighbor methods clustering algorithms Three-dimensional displays Surface treatment Accuracy Robots Manuals Noise Manufacturing Defect diagnosis clustering algorithm automatic optical inspection point cloud

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Between/Within View Information Completing for Tensorial Incomplete Multi-View clustering

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IEEE TRANSACTIONS ON MULTIMEDIA 2025年 27卷 1538-1550页

作者： Yao, Mingze Wang, Huibing Chen, Yawei Fu, Xianping Dalian Maritime Univ Coll Informat Sci & Technol Dalian 116026 Liaoning Peoples R China

Incomplete Multi-view clustering (IMvC) receives increasing attention due to its effectiveness in solving data-missing problems. With the information loss in incomplete situations, the core of IMvC needs to consider effectively overcoming the challenge of missing views, that is, exploring the underlying correlations from available data and recovering the missing information. However, most existing IMvC methods overemphasize the recovery-first principle with integrating the existing data from different views while neglecting the influence of view consistency in IMvC task together with valuable within view information. In this paper, a novel Between/Within View Information Completing for Tensorial Incomplete Multi-view clustering (BWIC-TIMC) has been proposed, in which between/within view information is jointly exploited for effectively completing the missing views. Specifically, the proposed method designs a dual tensor constraint module, which focuses on simultaneously exploring the view-specific correlations of incomplete views and enforcing the between view consistency across different views. With the dual tensor constraint, between/within view information can be effectively integrated for completing missing views for IMvC task. Furthermore, in order to balance different contributions of multiple views and alleviate the problem of feature degeneration, BWIC-TIMC implements an adaptive fusion graph learning strategy for consensus representation learning. Extensive comparative experiments with the-state-of-art baselines can demonstrate the effectiveness of BWIC-TIMC.

关键词： Tensors Correlation Bipartite graph Perturbation methods Kernel Fast Fourier transforms clustering methods clustering algorithms Web pages Vectors Between/within view information incomplete multi-view clustering (IMvC) dual tensor constraint

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A Spatial Model and Multiobjective Fuzzy Inference-Based clustering Routing Algorithm for Bridge Monitoring

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IEEE SENSORS JOURNAL 2025年第1期25卷 1669-1681页

作者： Yang, Jiguang Huo, Jiuyuan Mu, Cong Lanzhou Jiaotong Univ Sch Elect & Informat Engn Lanzhou 730070 Peoples R China Lanzhou Jiaotong Univ Sch Elect & Informat Engn Lanzhou 730070 Peoples R China Natl Cryosphere Desert Data Ctr NCDC Lanzhou 730000 Peoples R China

Wireless sensors have the advantages of distributed and flexible deployment, real-time monitoring, multisensor collaboration, self-organization, and self-adaptation. They can be widely used in bridge structural health monitoring systems (BHMSs). At the same time, wireless sensors are still limited by battery life and communication distance;extending the life cycle of wireless sensor networks (WSNs) employing optimal deployment and effective improvement of routing algorithms is a research hotspot for domestic and foreign engineering and academic circles. In this article, a WSN clustering routing algorithm based on bridge spatial model and multiobjective fuzzy inference (BSMOFC) is proposed to solve the problem that the existing research has not considered the spatial characteristics of bridges and the reasonable choice of cluster head (CH) in the clustering routing algorithm. First, a bridge space model is designed to simulate the 3-D deployment of WSN nodes;second, the CH selection process of the WSN clustering routing algorithm is defined as a multiobjective optimization problem, and the multiobjective non-dominated sorted whale optimization algorithm (NSWOA) is used to solve the set of Pareto solutions. All the solutions are defined as the candidate CHs (C-CHs), and then a fuzzy inference system is used to choose the final CHs (F-CHs). Finally, the simulation platform MATLAB completes the performance verification of the BSMOFC algorithm. The simulation results show that the BSMOFC algorithm can effectively extend the WSN lifetime and provide a new practical method for selecting a Pareto optimal solution.

关键词： Wireless sensor networks clustering algorithms Bridges Routing Optimization Monitoring Inference algorithms Wireless communication Fuzzy logic Sensor phenomena and characterization Bridge space model bridge structural health monitoring system (BHMS) fuzzy inference system multiobjective optimization wireless sensor network (WSN)

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