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clustering of Diverse Multiplex Networks

IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第4期11卷 3441-3454页

作者： Pensky, Marianna Wang, Yaxuan Univ Cent Florida Dept Math Orlando FL 32816 USA

The paper introduces the DIverse MultiPLEx Generalized Random Dot Product Graph (DIMPLE-GRDPG) network model where all layers of the network have the same collection of nodes and follow the Generalized Random Dot Product Graph (GRDPG) model. In addition, all layers can be partitioned into groups such that the layers in the same group are embedded in the same ambient subspace but otherwise all matrices of connection probabilities can be different. In a common particular case, where layers of the network follow the Stochastic Block Model, this setting implies that the groups of layers have common community structures but all matrices of block connection probabilities can be different. We refer to this version as the DIMPLE model. While the DIMPLE-GRDPG model generalizes the COmmon Subspace Independent Edge (COSIE) random graph model, the DIMPLE model includes a wide variety of SBM-equipped multilayer network models as its particular cases. In the paper, we introduce novel algorithms for the recovery of similar groups of layers, for the estimation of the ambient subspaces in the groups of layers in the DIMPLE-GRDPG setting, and for the within-layer clustering in the case of the DIMPLE model. We study the accuracy of those algorithms, both theoretically and via computer simulations. The advantages of the new models are demonstrated using simulations and real data examples.

关键词： Multiplexing Tensors Stochastic processes Load modeling clustering algorithms Nonhomogeneous media Computational modeling Community detection multiplex network spectral clustering stochastic block model

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A Fast Pilot Assignment for Cell-Free Massive MIMO: Using Anchor-Based clustering Scheme

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IEEE WIRELESS COMMUNICATIONS LETTERS 2025年第4期14卷 1109-1113页

作者： Zhao, Yu Zhang, Fengming Gao, Yangjun Hu, Gang Air Force Engn Univ Dept Equipment Management & Unmanned Aerial Vehicl Xian 710043 Peoples R China

In this letter, we study the pilot assignment (PA) in cell-free (CF) massive multiple-input multiple-output (MIMO). PA is a key issue to facilitate signal processing, e.g., beamforming, by providing reliable channel estimation at access points (APs). In the context of non-orthogonal pilots, we propose an anchor-based (AB) clustering scheme to reduce the pilot contamination (PC) among the user equipments (UEs). The proposed approach aims at partitioning the potential high-PC UEs into different clusters. More specifically, we first construct an interference graph to model the PC. Then a binary space partitioning (BSP) algorithm is leveraged for generating clustering anchors. To obtain the target clustering of UEs, we perform a spectral analysis on the UE-anchor clustering matrix. Comprehensive numerical results on several different configuration settings, show that the proposed approach achieves near-optimal performance in terms of the normalized mean square error (NMSE) of channel estimation.

关键词： Interference Channel estimation Massive MIMO Array signal processing Signal to noise ratio Payloads Data communication Contamination clustering algorithms Vectors Pilot assignment anchor-based clustering massive MIMO

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An Efficient and Distributed Framework for Real-Time Trajectory Stream clustering

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第5期36卷 1857-1873页

作者： Gao, Yunjun Fang, Ziquan Xu, Jiachen Gong, Shenghao Shen, Chunhui Chen, Lu Zhejiang Univ Hangzhou 310027 Zhejiang Peoples R China Alibaba Grp Hangzhou 310052 Zhejiang Peoples R China AZFT Lab Hangzhou 310007 Zhejiang Peoples R China

With the explosive ubiquity of GPS-equipped devices, e.g., mobile phones, vehicles, and vessels, a massive amount of real-time, unbounded, and varying-sampling trajectory streams are being generated continuously. clustering trajectory streams is useful in real-life applications, such as traffic congestion prediction, crowd flow detection, and moving behavior study. Although several sliding-window based algorithms (that adopt the classic two-phases online-offline processing framework) are proposed for trajectory stream clustering, three challenges exist to meet ever-increasing application demands for effective, efficient, and scalable online clustering: i) How to effectively model unbounded trajectory streams in the online settings for effective clustering? ii) How to achieve truly real-time online processing? iii) How to improve the scalable capability of the clustering algorithm to support large-scale moving trajectory streams? In this paper, we propose an efficient and distributed trajectory stream clustering framework that can: i) model trajectory streams dynamically and effectively in a self-adaptive manner, i.e., k-Segment, which considers both spatial and temporal aspects of trajectory streams, ii) support distributed indexing, processing, and workload balance, and iii) incrementally cluster trajectory streams in an efficient manner. Experiments on a wide range of real-world trajectory datasets show that our framework outperforms state-of-the-art baselines in terms of clustering quality, efficiency, and scalability.

关键词： Trajectory Real-time systems clustering algorithms Market research Scalability Behavioral sciences Measurement DBSCAN distributed online processing grid partitioning trajectory stream clustering

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A New Particle Swarm Optimization Algorithm for Outlier Detection: Industrial Data clustering in Wire Arc Additive Manufacturing

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2024年第2期21卷 1244-1257页

作者： Fang, Jingzhong Wang, Zidong Liu, Weibo Lauria, Stanislao Zeng, Nianyin Prieto, Camilo Sikstrom, Fredrik Liu, Xiaohui Brunel Univ London Dept Comp Sci Uxbridge UB8 3PH Middx England Xiamen Univ Dept Instrumental & Elect Engn Xiamen 361005 Fujian Peoples R China AIMEN Technol Ctr E-36418 Pontevedra Spain Univ West Dept Engn Sci S-46132 Trollhattan Sweden

In this paper, a novel outlier detection method is proposed for industrial data analysis based on the fuzzy C-means (FCM) algorithm. An adaptive switching randomly perturbed particle swarm optimization algorithm (ASRPPSO) is put forward to optimize the initial cluster centroids of the FCM algorithm. The superiority of the proposed ASRPPSO is demonstrated over five existing PSO algorithms on a series of benchmark functions. To illustrate its application potential, the proposed ASRPPSO-based FCM algorithm is exploited in the outlier detection problem for analyzing the real-world industrial data collected from a wire arc additive manufacturing pilot line in Sweden. Experimental results demonstrate that the proposed ASRPPSO-based FCM algorithm outperforms the standard FCM algorithm in detecting outliers of real-world industrial data.

关键词： clustering algorithms Anomaly detection Switches Metals Convergence Wires Particle swarm optimization Industrial data analysis outlier detection fuzzy C-means particle swarm optimization wire arc additive manufacturing

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Robust Tensor Subspace Learning for Incomplete Multi-View clustering

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第11期36卷 6934-6948页

作者： Liang, Cheng Wang, Daoyuan Zhang, Huaxiang Zhang, Shichao Guo, Fei Shandong Normal Univ Sch Informat Sci & Engn Jinan 250358 Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China

Incomplete multi-view clustering has represented a significant role in grouping real images. In this study, a novel robust tensor subspace learning (RTSL) is proposed for incomplete multi-view clustering. Specifically, the missing samples within views are first recovered by matrix factorization. The recovered information is utilized for latent representations learning. And then, the obtained latent representations are organized from all views into a third-order tensor and the intrinsic sample relations are captured with tensor linear representation. Moreover, a low-rank sample coefficient tensor is sought to capture high-order connections among views by imposing the tensor nuclear norm. Compared with traditional learning paradigms in the vector space, the sample relations within each view as well as across views could be preserved with the aid of robust tensor subspace learning. As a result, our model can simultaneously handle the missing samples and exploit the intrinsic correlations, leading to enhanced representation capability and better quality of the recovered data. We design an efficient iterative optimization strategy to solve the proposed method. Experimental results on eight datasets show that our model outperforms other competing approaches.

关键词： Tensors Data models Correlation clustering algorithms Sparse matrices Representation learning Optimization Matrix factorization missing-view inferring tensor subspace learning

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Tensorized and Compressed Multi-View Subspace clustering via Structured Constraint

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2024年第12期46卷 10434-10451页

作者： Chang, Wei Chen, Huimin Nie, Feiping Wang, Rong Li, Xuelong Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Peoples R China

Multi-view learning has raised more and more attention in recent years. However, traditional approaches only focus on the difference while ignoring the consistency among views. It may make some views, with the situation of data abnormality or noise, ineffective in the progress of view learning. Besides, the current datasets have become high-dimensional and large-scale gradually. Therefore, this paper proposes a novel multi-view compressed subspace learning method via low-rank tensor constraint, which incorporates the clustering progress and multi-view learning into a unified framework. First, for each view, we take the partial samples to build a small-size dictionary, which can reduce the effect of both redundancy information and computation cost greatly. Then, to find the consistency and difference among views, we impose a low-rank tensor constraint on these representations and further design an auto-weighted mechanism to learn the optimal representation. Last, due to the non-square of the learned representation, the bipartite graph has been introduced, and under the structured constraint, the clustering results can be obtained directly from this graph without any post-processing. Extensive experiments on synthetic and real-world benchmark datasets demonstrate the efficacy and efficiency of our method, especially for the views with noise or outliers.

关键词： Dictionaries Tensors Costs Bipartite graph Optimization clustering methods clustering algorithms Compressed dictionary representation Laplacian rank constraint low-rank tensor learning multi-view subspace learning optimal bipartite graph

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Enhancing DBSCAN clustering for Fingerprint-Based Localization With a Context Similarity Coefficient-Based Similarity Measure Metric

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IEEE ACCESS 2024年 12卷 117298-117307页

作者： Yaro, Abdulmalik Shehu Maly, Filip Maly, Karel Prazak, Pavel Univ Hradec Kralove Fac Informat & Management Dept Informat & Quantitat Methods Hradec Kralove 50003 Czech Republic Ahmadu Bello Univ Dept Elect & Telecommun Engn Zaria 810106 Nigeria

In fingerprint-based localization systems, clustering fingerprint databases is a proposed technique for improving localization accuracy while reducing localization time. Among various clustering algorithms, density-based spatial clustering of applications with noise (DBSCAN) stands out for its robustness to outliers and ability to accommodate fingerprint databases of various shapes. However, the clustering performance of the DBSCAN algorithm is heavily influenced by the type of similarity measure metric used, with most researchers using distance-based metrics. This paper aims to enhance DBSCAN clustering by using a pattern-based metric known as the context similarity coefficient (CSC) instead of distance-based metrics. The CSC metric examines received signal strength (RSS) measurement patterns that form fingerprint vectors and assesses both linear and non-linear relationships between these vectors to determine similarity. Four publicly available fingerprint databases were used to evaluate the clustering performance with silhouette scores as a performance metric. The performance of the DBSCAN algorithm with the CSC metric is determined and compared to Euclidean and Manhattan distances as similarity measure metrics. Simulation results indicate that achieving good clustering performance with the DBSCAN algorithm requires generating three or fewer clusters. The proposed CSC metric demonstrated the best clustering performance in two of four fingerprint databases and the second-best in another. However, computational complexity comparisons reveal that the CSC metric is highly computationally intensive and is suggested to be used on small to medium-sized fingerprint databases generated using an odd number of wireless APs deployed in a non-uniform or non-grid-like distribution.

关键词： Fingerprint recognition Measurement clustering algorithms Databases Location awareness Wireless communication Context modeling clustering context similarity coefficient DBSCAN distance-based metrics fingerprinting RSS

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Parameter-Insensitive Min Cut clustering With Flexible Size Constrains

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2024年第8期46卷 5479-5492页

作者： Nie, Feiping Xie, Fangyuan Yu, Weizhong Li, Xuelong Northwestern Polytech Univ Sch Artificial Intelligence OPt & Elect iOPEN Xian 710072 Peoples R China Northwestern Polytech Univ Key Lab Intelligent Interact & Applicat Minist Ind & Informat Technol Xian 710072 Peoples R China

clustering is a fundamental topic in machine learning and various methods are proposed, in which K-Means (KM) and min cut clustering are typical ones. However, they may produce empty or skewed clustering results, which are not as expected. In KM, the constrained clustering methods have been fully studied while in min cut clustering, it still needs to be developed. In this paper, we propose a parameter-insensitive min cut clustering with flexible size constraints. Specifically, we add lower limitations on the number of samples for each cluster, which can perfectly avoid the trivial solution in min cut clustering. As far as we are concerned, this is the first attempt of directly incorporating size constraints into min cut. However, it is a NP-hard problem and difficult to solve. Thus, the upper limits is also added in but it is still difficult to solve. Therefore, an additional variable that is equivalent to label matrix is introduced in and the augmented Lagrangian multiplier (ALM) is used to decouple the constraints. In the experiments, we find that the our algorithm is less sensitive to lower bound and is practical in image segmentation. A large number of experiments demonstrate the effectiveness of our proposed algorithm.

关键词： clustering algorithms Optimization clustering methods NP-hard problem Vectors Task analysis Symmetric matrices Augmented Lagrangian multiplier (ALM) clustering empty cluster flexible size constrains min cut

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Low-Energy Resource Classification Algorithm for Cross-Regional Cloud Data Centers Based on K-Means clustering Algorithm

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第8期20卷 10084-10091页

作者： Liang, Bin Bai, Junqing Xian Shiyou Univ Sch Comp Sci Xian 710065 Peoples R China

As the division of labor in the industry becomes more refined, an increasing number of companies are abandoning infrastructure construction and instead moving their operations to cloud data centers (CBDCs). Cloud service providers are responding to the surge in demand by deploying their own CBDCs worldwide. However, the energy consumption and operation costs of these CBDCs vary depending on the region's environment and policies. To mitigate these costs, cloud service providers often employ resource management algorithms. This article conducts a comprehensive analysis of the cross-regional CBDC model, including establishing virtual machine classification rules based on clustering results. Ultimately, this article proposes a low-energy resource classification algorithm for cross-regional CBDCs based on the K-means clustering algorithm (LCKC). The effectiveness of the LCKC algorithm is compared to that of other algorithms, and the results indicate that it reduces energy consumption in cross-regional CBDCs.

关键词： Cloud computing clustering algorithms Energy consumption Costs Task analysis Data centers Computational modeling Cloud data centers (CBDC) clustering algorithm cross-regional energy consumption optimization resource classification

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ISAR Weak Feature Enhancement With Perturbation Defense Using Hybrid clustering Oversegmentation

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2024年第5期60卷 6256-6274页

作者： Xue, Bin Zheng, Qinghua Li, Zhinan Wang, Jianshan Mu, Chunwang Yang, Jungang Feng, Xue Fan, Hongqi Li, Xiang Xi An Jiao Tong Univ Xian 710049 Peoples R China Natl Univ Def Technol Wuhan 430010 Peoples R China Tsinghua Univ Beijing 100084 Peoples R China Tongji Univ Shanghai 200092 Peoples R China Natl Univ Def Technol Changsha 410073 Peoples R China

Weak feature enhancement is an important and intractable issue in machine vision fields. In this article, an effective complex-scene inverse synthetic aperture radar image feature enhancement method is proposed based on spatial neighborhood sparse constraint hybrid clustering oversegmentation (SNSC-HCOS). A hybrid superpixel concept is first developed considering the consistency of pixel labels and the discrimination of perturbations. The scattering characteristic similarity, spatial proximity, neighborhood information, sparsity bias, and overall image features are simultaneously explored. The SNSC-HCOS algorithm is designed based on the hybrid superpixel concept, data reconstruction, and fuzzy c-means (FCM) clustering. Both sparsity constraints and local spatial information constraints are embedded into the oversegmentation framework, improving the perturbation defense capability and clustering robustness. To correct misclassified pixel membership and improve system efficiency, median membership filtering and area merging strategies are further applied. Extensive qualitative and quantitative experiments are conducted on a real-scene dataset, demonstrating that SNSC-HCOS greatly outperforms the outstanding comparison systems. The proposed system has good segmentation performance, can generate more pure superpixels with regular shapes and uniform sizes, and has perturbation defense and feature enhancement capabilities.

关键词： Perturbation methods Image reconstruction Aerospace and electronic systems Hybrid power systems clustering algorithms Annotations Shape

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