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Dual-Structural Bipartite Graph Learning for Multiview clustering

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025年第6期PP卷 PP页

作者： Wei, Xiaohui Liu, Haibo Duan, Puhong Li, Shutao Hunan Normal Univ Coll Informat Sci & Engn Changsha 410081 Peoples R China Hunan Univ Coll Robot Changsha 410082 Peoples R China Coll Elect & Informat Engn Changsha 410082 Peoples R China

Bipartite graph (BiG) has been proven to be efficient in handling massive multiview data for clustering. However, how to regulate the structural information of view-specific anchors and view-shared BiG is still open and needs to be further studied. Hence, a novel dual-structural BiG learning (DsBiGL) method is proposed in the article. It transforms BiG learning into a joint optimization problem of IntrA-view and InteR-view subspace learning (IASL and IRSL) with the structural constraints, such as k-nearest neighbor (KNN) and low-rank. On one hand, IASL uses the KNN and view-specific low-rank constraints to enhance the discriminativeness of view-specific anchors. On the other hand, IRSL uses an adaptive weighting strategy to obtain view-shared BiG directly from multiview samples, where the KNN and view-shared low-rank constraints are adopted to encode local connectivity and cluster information between samples. Note that IASL and IRSL are integrated into a unified optimization model, which ensures the interactive enhancement of view-specific anchor representation and view-shared BiG learning. Finally, an algorithm based on iterative optimization is designed to solve the proposed DsBiGL model. Experimental results on various multiview datasets have demonstrated the superiority of DsBiGL in terms of clustering results when compared with other comparative methods.

关键词： Nearest neighbor methods Optimization Noise Learning systems Bipartite graph Accuracy Vectors clustering algorithms Iterative algorithms Electronic mail Bipartite graph (BiG) learning k-nearest neighbor (KNN) constraint low-rank constraint multiview clustering (MVC) subspace learning

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Expectation Distance-Based Distributional clustering for Noise-Robustness

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第11期36卷 6099-6110页

作者： Adesunkanmi, Rahmat Kumar, Ratnesh Iowa State Univ Dept Elect & Comp Engn Ames IA 50011 USA

This paper presents a clustering technique that reduces the susceptibility to data noise by learning and clustering the data-distribution and then assigning the data to the cluster of its distribution. In the process, it reduces the impact of noise on clustering results. This method involves introducing a new distance among distributions, namely the expectation distance (denoted, ED), that goes beyond the state-of-art distribution distance of optimal mass transport, also called 2-Wasserstein (denoted, W-2): The latter essentially depends only on the marginal distributions while the former also employs the information about the joint distributions, making it more powerful. Using the ED, the paper extends the classical K-means and K-medoids clustering to those over data-distributions (rather than raw-data) and further introduces K-medoids using W-2. The paper also presents the closed-form expressions of the W-2 and ED distance measures. The implementation results of the proposed ED and the W-2 distance measures to cluster real-world weather data as well as stock data are also presented, which involves efficiently extracting and using the underlying data distributions-Gaussians for weather data versus lognormals for stock data. The results show striking performance improvement over classical clustering of raw-data, with higher accuracy realized for ED. Also, not only does the distribution-based clustering offer higher accuracy, but it also lowers the computation time due to reduced time-complexity.

关键词： Meteorology Temperature distribution Costs Precipitation Noise clustering algorithms Noise measurement expectation distance Wasserstein distance uncertain data

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Energy-Distance-Function-Based Improved K-Means for clustering Routing Algorithm

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IEEE INTERNET OF THINGS JOURNAL 2024年第22期11卷 36763-36774页

作者： Zhou, Lin Zhang, Meng Wei, Qian Jin, Yong Wang, Saidi Yan, Jiayuan Henan Univ Sch Artificial Intelligence Zhengzhou 450046 Peoples R China

In wireless sensor networks (WSNs), conventional clustering routing algorithms often lead to uneven distribution of cluster heads (CHs), resulting in imbalanced and high-energy consumption. Addressing these challenges, this study introduces a novel algorithm, termed the energy distance function-based improved K-means algorithm (EDFIKM). This algorithm synthesizes the principles of the low-energy adaptive clustering hierarchy (LEACH) with the K-means clustering methodology. Key to this approach is the formulation of an adaptive weighted energy distance function that accounts for nodes' residual energy, their proximity to cluster centers, and dynamic weights for energy and distance factors. This function strategically mitigates the randomness in CH selection. Incorporating this function into the K-means clustering process achieves a more uniform distribution of CHs and an equitable allocation of nodes across clusters. Importantly, this contributes to a balanced energy load and a reduction in overall network energy consumption. Simulation studies validate that the EDFIKM algorithm efficiently lowers network energy demands and extends the network lifespan, without adding to the time complexity.

关键词： clustering algorithms Energy consumption Wireless sensor networks Adaptation models Routing Internet of Things Energy efficiency Cluster head (CH) selection energy consumption energy distance function K-means clustering wireless sensor network (WSN)

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A Metaheuristic Search Algorithm Based on Sampling and clustering

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IEEE ACCESS 2024年 12卷 15493-15508页

作者： Harita, Maria Wong, Alvaro Suppi, Remo Rexachs, Dolores Luque, Emilio Univ Autonoma Barcelona Dept Comp Architecture & Operating Syst Bellaterra 08193 Spain

As optimization problems become more complicated and extensive, parameterization becomes complex, resulting in a difficult task requiring significant amounts of time and resources. In this paper we propose a heuristic search algorithm we call MCSA (Montecarlo-clustering Search Algorithm), which is based on Montecarlo sampling, and a clustering strategy involving two techniques. Our objective is to apply MCSA, an inherently stochastic method, to address optimization problems. To assess its performance, we conducted an evaluation using classical benchmark optimization functions. Additionally, we leveraged the CEC2017 benchmark suite to comprehensively evaluate the algorithm, highlighting the pivotal role of the Exploration stage in our methodology. Subsequently, we extended our methodology to tackle a practical combinatorial problem, the Knapsack problem. This NP-Hard problem holds significant real-world applications in resource allocation, scheduling, planning, logistics, and more. Our contributions lie in parameterizing the Knapsack Problem to align with MCSA's parameters for reference indicators adjustment and achieving high-quality solutions, surpassing 90% in comparison to exhaustive methods such as branch and bound.

关键词： clustering algorithms Optimization Heuristic algorithms Metaheuristics Search problems Benchmark testing Partitioning algorithms Benchmarks heuristic methods Knapsack problem Montecarlo and clustering methods optimization

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Self-Supervised Deep Multiview Spectral clustering

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第3期35卷 4299-4308页

作者： Zong, Linlin Miao, Faqiang Zhang, Xianchao Liang, Wenxin Xu, Bo Dalian Univ Technol Sch Software Key Lab Ubiquitous Network & Serv Software Liaoni Dalian 116620 Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Peoples R China Inst Farmland Irrigat CAAS Xinxiang 453003 Henan Peoples R China Dalian Univ Technol Sch Software Dalian 116620 Peoples R China Dalian Univ Technol Sch Comp Sci & Technol Dalian 116024 Peoples R China

Multiview spectral clustering has received considerable attention in the past decades and still has great potential due to its unsupervised integration manner. It is well known that pairwise constraints boost the clustering process to a great extent. Nevertheless, the constraints are usually marked by human beings. To ameliorate the performance of multiview spectral clustering and alleviate the consumption of human resources, we propose self-supervised multiview spectral clustering with a small number of automatically retrieved pairwise constraints. First, the fused multiple autoencoders are used to extract the latent consistent feature of multiple views. Second, the pairwise constraints are achieved based on the commonality among multiple views. Then, the pairwise constraints are propagated through the neural network with historical memory. Finally, the propagated constraints are used to optimize the fused affinity matrix of spectral clustering. Our experiments on four benchmark datasets show the effectiveness of our proposed approach.

关键词： Feature extraction clustering algorithms Task analysis Data mining Software Decoding Matrix decomposition Constraint propagation network deep multiview self-supervised spectral clustering

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One-pass View-unaligned clustering

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IEEE TRANSACTIONS ON MULTIMEDIA 2024年 26卷 9699-9709页

作者： Dong, Wenhua Wu, Xiao-Jun Feng, Zhenhua Ahmed, Sara Atito Ali Awais, Muhammad Kittler, Josef Jiangnan Univ Sch Sci Wuxi 214122 Peoples R China Univ Surrey Ctr Vis Speech & Signal Proc Guildford GU2 7XH England Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Peoples R China Univ Surrey Sch Comp Sci & Elect Engn Guildford GU2 7XH England

Given a set of multi-view instances, the prevailing assumption in most existing clustering approaches is that they are complete and exhibit cross-view alignment. However, this assumption is often unrealistic. In such scenarios, it could be satisfied at the cost of data pre-processing, but this would be complex and inconsistent with practical applications. Therefore, developing more effective solutions for the View-unaligned Problem (VuP) is highly desirable. Several pioneering works have tackled the partially VuP, yet handling fully VuP remains a challenge due to the reliance on partially pre-aligned instances. In this paper, we propose One-pass View-unaligned clustering (OpVuC) that simultaneously aligns and clusters instances in a unified framework. Specifically, we align shuffled instances with a selected template using an innovative global-local alignment scheme based on the notion of geometric invariance and separate the fully aligned instances using a relaxed $k$-means algorithm. The proposed OpVuC method can handle VuP at any alignment level without requiring any pre-aligned instances. Extensive experiments conducted on several benchmark datasets demonstrate the effectiveness and merits of the proposed OpVuC method.

关键词： clustering methods clustering algorithms Vectors Scalability Noise robustness Task analysis Speech recognition Multi-view clustering view-unaligned problem global-local alignment relaxed k-means

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One-Step Joint Learning of Self-Supervised Spectral clustering With Anchor Graph and Fuzzy clustering for Land Cover Classification

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2024年 17卷 11178-11193页

作者： Wu, Chengmao Zhang, Jiale Xian Univ Posts & Telecommun Xian 710121 Peoples R China

Spectral clustering, as an algorithm based on graph theory and spectral theory, has shown excellent performance in classification tasks of hyperspectral images in recent years. Although better results have been achieved, some challenges still exist. The inclusion of a priori information can increase the performance of spectral clustering algorithms;however, in practice, it is often unable to meet the demand for a priori information;at the same time, the two-stage structure of spectral clustering leads to bias and information loss during its clustering process. To address the above problems, we propose a one-step joint learning of self-supervised spectral clustering with anchor graph and fuzzy clustering. In this algorithm, we first use the BKHK method to generate anchor points that vary with the size of the dataset to enhance the quality of the similarity graph. After that, we use the idea of fuzzy clustering to optimize the membership and simultaneously learn the local and global structures of the data. Moreover, clustering information is integrated with pairwise constraint information in a unified update framework, which achieves mutual learning of clustering results and constraints under soft partitioning. After comparing with the existing algorithms, the proposed algorithm has better performance in classifying numerical datasets, remote sensing images, and hyperspectral datasets.

关键词： clustering algorithms Hyperspectral imaging Partitioning algorithms Classification algorithms Unsupervised learning Training Optimization Anchor graph fuzzy clustering hyperspectral image self-supervised spectral clustering

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Strong Consistency of Spectral clustering for the Sparse Degree-Corrected Hypergraph Stochastic Block Model

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IEEE TRANSACTIONS ON INFORMATION THEORY 2024年第3期70卷 1962-1977页

作者： Deng, Chong Xu, Xin-Jian Ying, Shihui Shanghai Univ Dept Math Shanghai 200444 Peoples R China Shanghai Univ Qianweichang Coll Shanghai 200444 Peoples R China

We prove strong consistency of spectral clustering under the degree-corrected hypergraph stochastic block model in the sparse regime where the maximum expected hyperdegree is as small as Omega (log n) with n denoting the number of nodes. We show that the basic spectral clustering without preprocessing or postprocessing is strongly consistent in an even wider range of the model parameters, in contrast to previous studies that either trim high-degree nodes or perform local refinement. At the heart of our analysis is the entry-wise eigenvector perturbation bound derived by the "leave-one-out"technique. To the best of our knowledge, this is the first entry-wise error bound for degree-corrected hypergraph models, resulting in the strong consistency for clustering non-uniform hypergraphs with heterogeneous hyperdegrees.

关键词： clustering algorithms Linear matrix inequalities Stochastic processes Perturbation methods Numerical models Image edge detection Approximation algorithms Hypergraph stochastic block model spectral clustering consistency

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Temporal and Multivariate Similarity clustering of 5G Performance Data

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IEEE ACCESS 2024年 12卷 114137-114145页

作者： Mazgula, Jakub Krol, Dariusz Jablonski, Ireneusz Wroclaw Univ Sci & Technol Fac Informat & Commun Technol PL-50370 Wroclaw Poland Nok Solut & Networks PL-54130 Wroclaw Poland Brandenburg Tech Univ Cottbus Fac Phys D-03046 Cottbus Germany Fraunhofer Inst Photon Microsyst Branch Integrated Silicon Syst D-03046 Cottbus Germany

The performance of 5G mobile network cells is highly impacted by their evolving configuration and temporal environmental conditions, such as the number of connected devices or resource utilization. Evaluation of the performance of such a system is a complex task that requires the simultaneous analysis of multiple indicators and inspires the research community to work on zero-touch network service management. In this paper, we present a novel time series clustering method - Temporal and Multivariate Similarity clustering (TMSC) - that incorporates Dynamic Time Warping with Limited Warping Length and Spectral clustering, allowing for radio cell grouping based on realization of multiple Key Performance Indicators. We evaluated TMSC against state-of-the-art algorithms at a practical task of identifying cell configuration differences by clustering their performance metrics with a limited set of observations. The proposed algorithm outperformed other methods regarding the Normalized Mutual Information score achieved for more than 95% of the cases studied. We also display the potential for method generalization by evaluating it at the hand gesture recognition task, which yields satisfactory results.

关键词： Time series analysis 5G mobile communication clustering algorithms Matrices Key performance indicator Cellular networks Vectors Spectral analysis Multivariate regression 5G network performance radio cell management spectral clustering dynamic time warping multivariate time series temporal similarity

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FSS-DBSCAN: Outsourced Private Density-Based clustering via Function Secret Sharing

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2024年 19卷 7759-7773页

作者： Fu, Jiaxuan Cheng, Ke Song, Anxiao Xia, Yuheng Chang, Zhao Shen, Yulong Xidian Univ Sch Comp Sci & Technol Xian 710071 Shaanxi Peoples R China Xian Univ Posts & Telecommun Shaanxi Key Lab Informat Commun Network & Secur Xian 710121 Peoples R China

Density-based clustering algorithms such as DBSCAN, are highly effective in handling large datasets and identifying clusters of arbitrary shapes, playing a crucial role in data analysis fields like outlier detection and social networks. Outsourcing DBSCAN to the cloud brings substantial benefits but also raises major privacy concerns regarding the private input data of data owners. Existing private DBSCAN methods often face challenges of inefficiency or potential privacy leakage, hindering their practical deployment. To address these challenges, we introduce FSS-DBSCAN, a three-server MPC platform designed for outsourced private density-based clustering using function secret sharing (FSS). This solution guarantees clustering quality equivalent to plaintext algorithms, ensures comprehensive privacy protection, and achieves top-tier efficiency. The high performance of FSS-DBSCAN is driven by two pivotal strategies. First, we devise an MPC-friendly DBSCAN algorithm that is highly compatible with efficient secret-sharing-based cryptographic protocols and benefits from GPU acceleration. Second, we construct novel FSS-based protocols tailored for complex operations integral to our DBSCAN variant, such as Euclidean distance comparison and point assignment, and further optimize their computation through tensorization techniques. We implement our platform as an extensible system on top of PyTorch that leverages GPU hardware acceleration for cryptographic and tensorized operations. These innovations enable FSS-DBSCAN to significantly outperform ppDBSCAN (AsiaCCS 2021), reducing the clustering time for 5000 samples to approximately 2 hours, achieving an $83.4\times $ speed improvement.

关键词： clustering algorithms Protocols Cryptography Graphics processing units Additives Outsourcing Distributed databases Secure clustering DBSCAN additive secret sharing secure comparison

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