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A High Stability clustering Scheme for the Internet of Vehicles

IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2024年第4期21卷 4297-4311页

作者： Chen, Chen Si, Jiabao Li, Huan Han, Wei Kumar, Neeraj Berretti, Stefano Wan, Shaohua Xidian Univ Sch Telecommun Engn Xian 710071 Peoples R China Xidian Guangzhou Inst Technol Intelligent Transportat Lab Guangzhou 510555 Peoples R China Xidian Hangzhou Inst Technol Automot Elect Res Inst Hangzhou 311231 Peoples R China Xian Beilin Univ Based Innovat Grp Co Ltd Xian 710071 Peoples R China Thapar Inst Engn & Technol Dept Comp Sci & Engn Patiala 147004 India Univ Florence Dept Informat Engn I-50139 Florence Italy Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen 518110 Peoples R China

In existing research on cluster head selection schemes in the Internet of Vehicles (IoV), designing a stable cluster structure poses a significant challenge. Choosing a centrally-located cluster head that can respond rapidly is crucial for meeting various requirements. To address the aforementioned challenges, this paper introduces a machine learning-based IoV cluster head selection scheme (HSCS). We introduce a new metric termed N-cycle Average Virtual Cluster Delay (XTn) for appropriate cluster head selection. To accommodate the high dynamism of vehicles, a machine learning model is integrated to predict cluster head selection metrics across different periods, and a set of cluster head selection guidelines is formulated. Experimental results demonstrate that our proposed HSCS ensures a relatively low average intra-cluster delay while maintaining a longer cluster head retention time, and it exhibits commendable robustness.

关键词： Magnetic heads clustering algorithms Heuristic algorithms Measurement Delays Stability criteria Optimization The Internet of Vehicles cluster head selection scheme machine learning

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A 3D Lidar Odometry and Mapping Method Based on clustering-Directed Feature Point

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第12期73卷 18391-18401页

作者： Su, Yukang Zhang, Shuo Zhang, Chengning Wu, Yuyang Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China Beijing Inst Technol Collaborat Innovat Ctr Elect Vehicles Beijing Beijing 100081 Peoples R China

Lidar odometry (LO) aims to provide robot attitude estimation, which is crucial for robot motion decision-making. In this paper, a new method of lidar odometry and mapping based on clustering-directed feature points (CDP-LOAM) is proposed. The proposed approach introduces a seven-dimensional clustering-directed point (CDP) that encompasses three-dimensional coordinate information, three-dimensional feature vector information, and one-dimensional point cloud category information of LiDAR points. Among them, the point cloud categories are obtained through ground point segmentation and point cloud clustering, and the feature vectors are obtained by processing the classified point clouds with principal component analysis (PCA). The paper proposes a two-step point cloud matching approach and establishes an adaptable weighted comprehensive residual that includes distance and direction residuals of the associated point cloud. The attitude estimation results are obtained by optimizing the residual error. The experimental results demonstrate that this method achieves more accurate positioning results than the traditional lidar method in a wider range of application scenarios.

关键词： Feature extraction Point cloud compression Laser radar Accuracy Vectors clustering algorithms Simultaneous localization and mapping Lidar odometry clustering-directed point two-step point cloud matching adaptable weighted comprehensive residual

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Optimizing Model Dissemination for Hierarchical clustering Learning in Edge Computing

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2024年第6期10卷 2397-2411页

作者： Zhang, Long Feng, Gang Qin, Zheng Li, Xiaoqian Univ Elect Sci & Technol China Natl Key Lab Wireless Commun Chengdu 611731 Peoples R China

Hierarchical clustering learning (HCL) extends traditional parameter server-based distributed learning by clustering heterogeneous user equipments (UEs) via cluster nodes (CNs) located at the edge of the network. Currently, most vanilla model dissemination strategies in distributed learning rely on one-to-many transmissions, inevitably consuming excessive precious bandwidth resources. Consequently, communication-efficiency becomes crucial for HCL in resource-constrained edge networks. In this paper, we propose a multistage cooperative model dissemination strategy to sequentially determine the subsets of CNs that can concurrently transmit models during individual scheduling stages, thereby improving communication efficiency in HCL. We formulate the strategy design as an optimization problem to minimize the maximum completion time of the slowest straggler in communication rounds, while accurately clustering UEs to CNs with similar data distributions. To make sequential and combinatorial decisions in individual stages, we develop an online learning algorithm, called sequential combinatorial multi-armed bandit (SCMAB). The SCMAB enables learning a multistage cooperative model dissemination strategy via an asymptotically optimal approach. Furthermore, the SCMAB dynamically re-clusters UEs to appropriate CNs, according to the similarity of UEs' data distribution. The simulation results indicate that compared to traditional transmission strategies, the proposed strategy improves communication efficiency by 2.11% to 5.57%, while achieving comparable and even higher learning accuracy.

关键词： Computational modeling Servers Data models Computer architecture clustering algorithms Heuristic algorithms Costs Hierarchical clustering learning distributed learning edge computing sequential combinatorial MAB

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DRSCDM: A Novel Density-Related clustering for Complex High-Dimensional Data Streams

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第12期34卷 12652-12664页

作者： Li, Dongdong Fan, Yihan Wang, Zhe East China Univ Sci & Technol Dept Comp Sci & Technol Shanghai 200237 Peoples R China

The proliferation of high-dimensional complex data in various fields such as multimedia, social media, and sensor networks has led to an increasing demand for real-time clustering algorithms. This article presents a novel two-stage approach for complex data streams. In the online stage, angular margin are introduced to constrain the mapping of input data, enhancing the directional characteristics of the resulting data representation. In the offline stage, we propose a unique clustering approach grounded in angular density to uncover spatial relationships within the data. This approach utilizes two distinct strategies for angular density clustering. Neighbor Selection based on Angular Relations define the angular density, which significantly enhances the algorithm's discriminative ability. Density-Priority Cluster Selection strategy determines the generation of clusters, ensuring the reliability of clustering. We also introduce a novel data expiration mechanism that optimizes computational costs and memory usage by discarding data objects from stable clusters. Experimental evaluations on four diverse datasets, including speaker diarization and video face clustering tasks, demonstrate the superior performance of our proposed method over state-of-the-art online clustering techniques. Furthermore, our method achieves comparable performance to offline clustering methods, highlighting its effectiveness and efficiency in real-time clustering applications. The source code for the proposed algorithms is accessible at https://***/sssssuda/DRSCDM.

关键词： Streams clustering algorithms Streaming media Feature extraction Circuits and systems Task analysis Heuristic algorithms Data stream clustering density-based clustering deep mapping softmax

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Can Evolutionary clustering Have Theoretical Guarantees?

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2024年第5期28卷 1220-1234页

作者： Qian, Chao Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Peoples R China

clustering is a fundamental problem in many areas, which aims to partition a given data set into groups based on some distance measure, such that the data points in the same group are similar while that in different groups are dissimilar. Due to its importance and NP-hardness, a lot of methods have been proposed, among which evolutionary algorithms (EAs) are a class of popular ones. Evolutionary clustering has found many successful applications, but all the results are empirical, lacking theoretical support. This article fills this gap by proving that the approximation performance of the global simple evolutionary multiobjective optimizer (GSEMO) (a simple multiobjective EA) for solving four formulations of clustering, i.e., k-tMM, k-center, discrete k-median, and k-means, can be theoretically guaranteed. Furthermore, we consider clustering under fairness, which tries to avoid algorithmic bias, and has recently been an important research topic in machine learning. We prove that for discrete k-median clustering under individual fairness, the approximation performance of the GSEMO can be theoretically guaranteed with respect to both the objective function and the fairness constraint.

关键词： clustering algorithms Linear programming Partitioning algorithms Evolutionary computation Data analysis Heuristic algorithms Euclidean distance clustering discrete k-median evolutionary algorithms (EAs) fairness k-tMM k-center k-means theoretical analysis

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Fitting Multiple Machine Learning Models With Performance Based clustering

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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 816-820页

作者： Lorasdagi, Mehmet E. Koc, Ahmet B. Koc, Ali T. Kozat, Suleyman S. Bilkent Univ Dept Elect & Elect Engn TR-06800 Ankara Turkiye

Traditional machine learning approaches assume that data comes from a single generating mechanism, which may not hold for most real life data. In these cases, the single mechanism assumption can result in suboptimal performance. We introduce a clustering framework that eliminates this assumption by grouping the data according to the relations between the features and the target values, and we obtain multiple separate models to learn different parts of the data. We further extend our framework to applications having streaming data where we produce outcomes using an ensemble of models. For this, the ensemble weights are updated based on the incoming data batches. We demonstrate the performance of our approach over the widely-studied real life datasets, showing significant improvements over the traditional single-model approaches.

关键词： Data models clustering algorithms Vectors Signal processing algorithms Wind turbines Training Predictive models Data mining Convergence Wind speed clustering expectation-maximization streaming data ensemble methods

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How to Construct Corresponding Anchors for Incomplete Multiview clustering

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第4期34卷 2845-2860页

作者： Yu, Shengju Wang, Siwei Wen, Yi Wang, Ziming Luo, Zhigang Zhu, En Liu, Xinwang Natl Univ Def Technol Sch Comp Changsha 410073 Peoples R China Intelligent Game & Decis Lab Beijing 100071 Peoples R China China Acad Aerosp Sci & Innovat Beijing 100176 Peoples R China

Anchor based incomplete multiview clustering has grasped growing interest recently because of its great success in effectively partitioning multimodal data. However, due to the absence of label information, the constructed anchors could be mismatched. Such an Anchor Mismatching Problem (AMP) will cause the structure of generated bipartite graph to be chaotic, degrading the clustering performance. To tackle this issue, we design an algorithm termed Constructing Corresponding Anchors for Incomplete Multiview clustering (CCA-IMC). Specifically, we first devise a permutation strategy to transform anchors on each view. Subsequently, we directly generate the consensus bipartite graph, which is shared for all incomplete views, by the transformed anchors rather than by fusing each view-specific bipartite graph. Afterwards, all anchors and permutation matrices as well as the consensus bipartite graph are jointly optimized in one common framework so as to promote each other. In such ways, anchors are rearranged towards correct matching relationship according to the consensus graph structure. In addition to these, our CCA-IMC has also been proven to be with linear time and memory overheads, which makes it able to scale up to work with large-scale tasks. Massive experiments implemented on ten popular datasets give evidence of our superiorities compared to current strong IMC competitors.

关键词： Bipartite graph clustering algorithms Transforms Task analysis Kernel Complexity theory Neural networks Incomplete multiview clustering corresponding anchors bipartite graph multiview learning

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Explainable Impact of Partial Supervision in Semi-Supervised Fuzzy clustering

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第5期32卷 3189-3198页

作者： Kmita, Kamil Kaczmarek-Majer, Katarzyna Hryniewicz, Olgierd Polish Acad Sci Syst Res Inst PL-01447 Warsaw Poland

Controlling the impact of partial supervision on the outcomes of modeling is of uttermost importance in semi-supervised fuzzy clustering. Semi-Supervised Fuzzy C-Means (SSFCMeans), a specific model we consider, uses a single hyperparameter called a scaling factor alpha to weigh the impact of partially labeled data. This concept became widespread and was reused directly in many works building on SSFCMeans, or even applied to other fuzzy clustering algorithms, such as Possibilistic C-Means. However, none of the works challenged the original interpretation of alpha, which suggests that the impact of partial supervision is directly proportional to the scaling factor. We fill the above-mentioned research gap and thoroughly analyze this relationship. We provide novel explanations of the scaling factor alpha in terms of the key element of fuzzy clustering-the membership values. We prove that the impact of partial supervision is a nonlinear function of alpha. Our approach is rooted in the explainability framework, which distinguishes interpretation from an explanation and treats the latter as superior. Explaining the scaling factor leads to an explainable impact of partial supervision and enables greater control of it. Finally, built on the novel explanations, we propose a unified, analytically justified framework for selecting the value of the hyperparameter alpha that is based on the cross-validation approach. We illustrate that the proposed framework enables an extensive analysis of the impact of partial supervision in SSFCMeans with a simulation experiment.

关键词： Additives Phase change materials Adaptation models Linear programming Prototypes Fuzzy systems clustering algorithms Explainable artificial intelligence Fuzzy C-Means (FCM) fuzzy clustering partial supervision Possibilistic C-Means (PCM) semi-supervised learning (SSL)

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Efficient Multi-View clustering via Unified and Discrete Bipartite Graph Learning

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第8期35卷 11436-11447页

作者： Fang, Si-Guo Huang, Dong Cai, Xiao-Sha Wang, Chang-Dong He, Chaobo Tang, Yong South China Agr Univ Coll Math & Informat Guangzhou 510642 Peoples R China Minist Agr & Rural Affairs Key Lab Smart Agr Technol Trop South China Guangzhou Peoples R China Sun Yat sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Guangdong Key Lab Informat Secur Technol Guangzhou 510006 Peoples R China South China Normal Univ Sch Comp Sci Guangzhou 510898 Peoples R China

Although previous graph-based multi-view clustering (MVC) algorithms have gained significant progress, most of them are still faced with three limitations. First, they often suffer from high computational complexity, which restricts their applications in large-scale scenarios. Second, they usually perform graph learning either at the single-view level or at the view-consensus level, but often neglect the possibility of the joint learning of single-view and consensus graphs. Third, many of them rely on the k-means for discretization of the spectral embeddings, which lack the ability to directly learn the graph with discrete cluster structure. In light of this, this article presents an efficient MVC approach via unified and discrete bipartite graph learning (UDBGL). Specifically, the anchor-based subspace learning is incorporated to learn the view-specific bipartite graphs from multiple views, upon which the bipartite graph fusion is leveraged to learn a view-consensus bipartite graph with adaptive weight learning. Furthermore, the Laplacian rank constraint is imposed to ensure that the fused bipartite graph has discrete cluster structures (with a specific number of connected components). By simultaneously formulating the view-specific bipartite graph learning, the view-consensus bipartite graph learning, and the discrete cluster structure learning into a unified objective function, an efficient minimization algorithm is then designed to tackle this optimization problem and directly achieve a discrete clustering solution without requiring additional partitioning, which notably has linear time complexity in data size. Experiments on a variety of multi-view datasets demonstrate the robustness and efficiency of our UDBGL approach. The code is available at https://***/huangdonghere/UDBGL.

关键词： Bipartite graph Laplace equations Optimization clustering algorithms Partitioning algorithms Linear programming Fuses Bipartite graph learning data clustering Index Terms large-scale clustering linear time multi-view clustering (MVC)

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Computationally Efficient Retrieval of Snow Surface Properties From Spaceborne Imaging Spectroscopy Measurements Through Dimensionality Reduction Using k-Means Spectral clustering

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2024年 17卷 8594-8605页

作者： Wilder, Brenton A. Lee, Christine M. Chlus, Adam Marshall, Hans-Peter Brandt, Jodi Kinoshita, Alicia M. Enterkine, Josh Van Der Weide, Thomas Glenn, Nancy F. Boise State Univ Dept Geosci Boise ID 83725 USA CALTECH Jet Prop Lab Pasadena CA 91125 USA Boise State Univ Human Environm Syst Boise ID 83725 USA San Diego State Univ Dept Civil Environm & Construct Engn San Diego CA 92182 USA

Snow albedo is a crucial component to the energy balance of our seasonal snowpack on Earth, reflecting incoming solar irradiance and altering the Earth system. Seasonal snow surface properties undergo constant change (e.g., melt-freeze cycle, faceting, sublimation, and windblown compaction) and have high spatial variability, especially in mountain regions, making it difficult to scale ground measurements. Snow albedo, fractional snow-covered area, and snow-specific surface area can be modeled using top of atmosphere radiance measurements from spaceborne imaging spectroscopy. We model these snow properties by testing inversions geared specifically for complex topography, as well as incorporating the impacts of the mixed snow pixel. Additionally, we avoid computing every pixel in an image by creating tens-of-thousands of k-means clusters based on the rounded values of the cosine of the local illumination angle to the nearest ten-thousandth digit. This computation is further sped up by leveraging the message passing interface to scale with more nodes. We present this work as an open-source algorithm, which we refer to as Global Optical Snow properties via High-speed Algorithm With K-means clustering (GOSHAWK). We validate our algorithm with PRecursore IperSpettrale della Missione Applicativa L1 radiance imagery across eight sites in the Northern Hemisphere from 2021 to 2023 and compare outputs with field spectroscopy, specific surface area measurements, airborne LiDAR surveys, and four-component net radiometers. More work in algorithm development and calibration-validation work is needed in steep terrain and dense canopy to improve snow property retrieval prior to the Surface Biology and Geology and Copernicus Hyperspectral imaging mission for the environment missions.

关键词： Snow clustering algorithms Surface topography Optimization Spectroscopy Lighting Imaging Imaging spectroscopy optimization snow albedo

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