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Fast Multiview clustering by Optimal Graph Mining

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第9期35卷 13071-13077页

作者： Lu, Jitao Nie, Feiping Wang, Rong Li, Xuelong Northwestern Polytech Univ Sch Comp Sci Sch Artificial Intelligence OPt & ElectroN iOPEN Key Lab Intelligent Interact & Applicat Xian 710072 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence OPt & Elect iOPEN Key Lab Intelligent Interact & Applicat Xian 710072 Peoples R China

Multiview clustering (MVC) aims to exploit heterogeneous information from different sources and was extensively investigated in the past decade. However, far less attention has been paid to handling large-scale multiview data. In this brief, we fill this gap and propose a fast multiview clustering by an optimal graph mining model to handle large-scale data. We mine a consistent clustering structure from landmark-based graphs of different views, from which the optimal graph based on the one-hot encoding of cluster labels is recovered. Our model is parameter-free, so intractable hyperparameter tuning is avoided. An efficient algorithm of linear complexity to the number of samples is developed to solve the optimization problems. Extensive experiments on real-world datasets of various scales demonstrate the superiority of our proposal.

关键词： clustering algorithms Time complexity Optimization clustering methods Task analysis Mathematical models Learning systems Anchor graph graph cut multiview clustering (MVC) scalable clustering

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Online Unsupervised Video Object Segmentation via Contrastive Motion clustering

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第2期34卷 995-1006页

作者： Xi, Lin Chen, Weihai Wu, Xingming Liu, Zhong Li, Zhengguo Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Anhui Univ Sch Elect Engn & Automat Hefei 230601 Peoples R China ASTAR SRO Dept Inst Infocomm Res Singapore 138632 Singapore

Online unsupervised video object segmentation(UVOS) uses the previous frames as its input to automatically separate the primary object(s) from a streaming video without using any further manual annotation. A major challenge is that the model has no access to the future and must rely solely on the history ,i.e., the segmentation mask is predicted from the current frame as soon as it is captured. In this work, a novel contrastive motion clustering algorithm with an optical flow as its input is proposed for the online UVOS by exploiting the common fate principle that visual elements tend to be perceived as a group if they possess the same motion pattern. We build a simple and effective auto-encoder to iteratively summarize non-learnable prototypical bases for the motion pattern, while the bases in turn help learn the representation of the embedding network .Further, a contrastive learning strategy based on a boundary prior is developed to improve foreground and background feature discrimination in the representation learning stage. The proposed algorithm can be optimized on arbitrarily-scale data(i.e., frame, clip, dataset) and performed in an online fashion. Experiments onDAVIS16,FBMS, andSegTrackV2datasets show that the accuracy of our method surpasses the previous state-of-the-art (SoTA) online UVOS method by a margin of 0.8%,2.9%, and 1.1%, respectively. Furthermore, by using an online deep subspace clustering to tackle the motion grouping, our method is able to achieve higher accuracy at 3xfaster inference time compared to SoTA online UVOS method, and making a good trade-off between effectiveness and efficiency.

关键词： Motion segmentation Optical flow Streaming media Prototypes Optimization Object segmentation clustering algorithms image motion analysis unsupervised learning self-supervised learning optical flow clustering methods

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Tensor Low-Rank Graph Embedding and Learning for One-Step Incomplete Multi-View clustering

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IEEE TRANSACTIONS ON MULTIMEDIA 2024年 26卷 9763-9775页

作者： Wan, Minghua Zhu, Jingyu Sun, Chengli Yang, Zhangjing Yin, Jun Yang, Guowei Nanjing Audit Univ Sch Comp Sci Sch Intelligent Audit Nanjing 211815 Peoples R China Shanghai Maritime Univ Coll Informat Engn Shanghai 201306 Peoples R China Nanjing Xiaozhuang Univ Key Lab Intelligent Informat Proc Nanjing 211171 Peoples R China Guangzhou Maritime Univ Sch Informat & Commun Engn Guangzhou 510700 Peoples R China Qingdao Univ Sch Elect Informat Qingdao 266071 Peoples R China

Traditional multi-view algorithms typically require data to be complete, and these algorithms may not be suitable or effective when dealing with incomplete data. As a result, the research field has witnessed the emergence of methods specifically designed for addressing incomplete multi-view clustering. In contrast, most of existing incomplete multi-view algorithms primarily emphasize capturing global information while neglecting the importance of local information. To overcome these challenges, we put forward a novel approach, named tensor low-rank graph embedding and learning for one-step incomplete multi-view clustering. We combine the beneficial aspects of graph embedding and tensor low-rank in this method, which not only focuses on local relationships but also provides insights into the global structure. Firstly, the initial similarity graph matrix is constructed using the inter-dependency between views. Secondly, the similarity graph matrix is structured as a third-order tensor and constrained by the tensor nuclear norm. This constraint enables the capturing of higher-order correlations across multiple views. Thirdly, graph embedding is added to obtain a common feature representation. Finally, the algorithm incorporates clustering to determine the optimal clustering labels. We conducted experiments comparing our algorithm with seven different incomplete multi-view methods using four different evaluation metrics. The experimental results indicate that our algorithm achieved the best clustering performance across five datasets under various missing rates. Especially when faced with a 50% missing rate in the Extended YaleB dataset, the clustering accuracy and normalized mutual information of our method are improved by 28.23% and 28.11%, respectively, compared to the second-best algorithm.

关键词： Tensors clustering algorithms clustering methods Correlation Computer science Symbols Sun Graph embedding incomplete multi-view multi-view clustering one-step tensor low-rank constraint

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Graph Distance and Adaptive K-Nearest Neighbors Selection-Based Density Peak clustering

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IEEE ACCESS 2024年 12卷 71783-71796页

作者： Sun, Yuqin Wang, Jingcong Sun, Yuan Zhang, Pengcheng Wang, Tianyi Shanghai Univ Elect Power Sch Math & Phys Shanghai 201306 Peoples R China

Density Peak clustering (DPC) is known for its rapid identification of cluster centers and successful clustering tasks. However, traditional DPC encounters several issues, which include simplifications in local density and distance metrics, a non-robust single-allocation strategy and limited fault tolerance. To address these challenges, this study introduces an innovative density peak clustering algorithm, named Graph Distance and Adaptive K-Nearest Neighbors Selection-Based Density Peak clustering (GAK-DPC). Our goal with the approach is to enhance the algorithm's adaptability to non-linear and complex data structures. We achieve this by replacing the traditional Euclidean distance with graph distance. Additionally, we redefine the method for computing local density based on information from K-nearest neighbor data points. By introducing the concept of natural neighbors, the neighborhood radius r is obtained when all instances in the dataset have at least one natural neighbor. Then for the current data point, the number of data points falling within a circle centered on it with radius r is counted as the K-value of that data point. Thus, we achieve the adaptive selection of the K-value. This adaptive K-value strategy takes into account the dataset's characteristics and inter-point neighbor relationships, which enhances the algorithm's adaptability and robustness. Finally, we optimize the secondary allocation strategy for sample points to improve the algorithm's fault tolerance. By conducting comparisons with traditional clustering algorithms on UCI datasets and synthetic datasets, we demonstrate the effectiveness of GAK-DPC.

关键词： clustering algorithms Partitioning algorithms Resource management Kernel Robustness Manifolds Euclidean distance Nearest neighbor methods Adaptive K-neighbors allocation strategy density peak clustering graph distance natural neighbors

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SS-DBSCAN: Semi-Supervised Density-Based Spatial clustering of Applications With Noise for Meaningful clustering in Diverse Density Data

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IEEE ACCESS 2024年 12卷 131507-131520页

作者： Zaki Abdulhameed, Tiba Yousif, Suhad A. Samawi, Venus W. Imad Al-Shaikhli, Hasnaa Al Nahrain Univ Coll Sci Comp Sci Dept Baghdad 64074 Iraq Isra Univ Dept Smart Business Amman 11622 Jordan

DBSCAN (Density-Based Spatial clustering of Applications with Noise) is an unsupervised clustering algorithm designed to identify clusters of various shapes and sizes in noisy datasets by pinpointing core points. The primary challenges associated with the DBSCAN algorithm involve the recognition of meaningful clusters within varying densities datasets and its sensitivity to parameter values of Epsilon distance and minimum number of neighbor points. These two issues may result in merging small clusters into larger clusters or splitting valid clusters into smaller clusters. A new Semi-Supervised DBSCAN (SS-DBSCAN) algorithm is introduced to improve the recognition of meaningful clusters. DBSCAN requires core points to be within, at most, Epsilon distance from their minimum neighboring points. The SS-DBSCAN algorithm, a modified version of the original DBSCAN, adds a pre-specified condition or constraint to identify core points further. This extra constraint is related to the clustering objective of a given dataset. To evaluate the effectiveness of SS-DBSCAN, we utilize three datasets: letter recognition, wireless localization, and Modern Standard Arabic (MSA) combined with Iraqi words language modeling. V-measure is used to evaluate the clustering efficiency for the letters recognition and wireless localization datasets. The perplexity (pp) of the class-based language model, built on the produced clusters, is the metric used for the Iraqi-MSA dataset clustering effectiveness. Experimental results showed the significant effectiveness of SS-DBSCAN. It outperforms DBSCAN when applied to letters and Iraqi-MSA datasets with improvements of 65% and 14.5%, respectively. A comparable performance was achieved when clustering the wireless localization dataset. Additionally, to assess the effectiveness of SS-DBSCAN, its performance has been compared to various modified versions of DBSCAN using four metrics: V-measure, PP, Adjusted Rand Index (ARI), and the Silhouette score. Base

关键词： clustering algorithms Noise measurement Measurement Complexity theory Classification algorithms Wireless sensor networks Semisupervised learning Unsupervised learning Text categorization clustering DBSCAN semi-supervised clustering unsupervised classification word classification

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A Novel Energy-Efficient Approach Based on clustering Using Gray Prediction in WSNs for IoT Infrastructures

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IEEE INTERNET OF THINGS JOURNAL 2024年第14期11卷 24748-24760页

作者： Luo, Haonan Wang, Jing Lin, Deyu Kong, Linghe Zhao, Yufei Guan, Yong Liang Nanchang Univ Sch Software Nanchang 330047 Peoples R China JiangXi Univ Software Profess Technol Nanchang Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

Wireless sensor networks (WSNs), which provide perception services for the Internet of Things (IoT) infrastructure, usually suffer from constrained energy resources. However, the fact that the data collected by WSNs often exhibit spatial-temporal correlation leads to the waste of energy. In addition, load imbalance among sensor nodes also makes energy efficiency low. To this end, a novel energy-efficient approach based on clustering using gray prediction (ECGP) is proposed in this article. To be specific, a novel dual-end data prediction mechanism (DDPM) is presented based on the gray prediction model to cut down data redundancy. Furthermore, the prediction process of the gray model, namely, a dynamic and fixed size prediction queue scheme, is optimized to enhance the prediction accuracy. A novel energy-distance factor (EDF) and a novel dual-threshold-based critical condition (DTCC) are proposed with the aim of realizing load balance and alleviating the challenge resulted from random events occurrence. Finally, extensive experimental simulations have been carried out to demonstrate the energy efficiency of ECGP. It is compared with the classic and several latest clustering algorithms, namely, low-energy adaptive clustering hierarchy (LEACH), R-LEACH, energy-aware hybrid approach, and energy efficient clustering algorithm based on particle swarm optimization technique. The experimental results indicate that ECGP outperforms the others in terms of the network lifetime, the throughput, and the energy efficiency.

关键词： Wireless sensor networks Predictive models Energy efficiency clustering algorithms Correlation Routing Data models Data prediction energy efficiency gray prediction model optimal cluster head (CH) selection

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Robust Discriminant Embedding Projection Fuzzy clustering With Optimal Mean

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第10期32卷 5924-5938页

作者： Wang, Jingyu Zhang, Xinru Nie, Feiping Li, Xuelong Northwestern Polytech Univ Sch Astronaut Sch Artificial Intelligence OPt & Elect iOPEN Xian 710072 Peoples R China Natl Key Lab Air Based Informat Percept & Fus Luoyang 471000 Peoples R China Northwestern Polytech Univ Sch Astronaut Xian 710072 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Peoples R China China Telecom Inst Artificial Intelligence TeleAI Beijing 100033 Peoples R China

The unsupervised nature of clustering has attracted significant interest. In particular, researchers delve into exploring the superiority of fuzzy clustering in flexibly handling computations involving uncertain data. However, outliers can present considerable challenges by distorting the measurement of similarity between samples, and biases in projection subspace learning may impede accurate partitioning. In this article, we propose a robust discriminant embedding projection fuzzy clustering with optimal mean (RPFCOM) method. First, the weighted loss function term distinguishes outliers and normal samples through boolean weight, thereby inducing row sparsity in the learning of projection subspace. The distribution of boolean weight penalizes outliers with large errors in the projection subspace. Second, we incorporate minimizing projection reconstruction information learning while suppressing redundant features, where the optimal mean dynamically corrects the projection learning bias. And the embedding of discriminative information further strengthens the capability of differentiating normal samples. Finally, the proposed method adaptively updates the boolean weight to identify outliers, which joints fuzzy membership matrix constructed from the maximum entropy graphs, enhancing the stability in distinguishing normal sample clusters. Comprehensive experimental validation on noise contaminated dataset has demonstrated the superiority of RPFCOM.

关键词： Noise Fuzzy systems clustering algorithms Vectors Heuristic algorithms Entropy clustering methods Fuzzy clustering robust discriminant embedding projection learning optimal mean

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Topology-Based clustering Techniques for Graph Partitioning Applied to the Italian Transmission Network

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IEEE ACCESS 2024年 12卷 84005-84019页

作者： Pomarico, Andrea Berizzi, Alberto Maria Giannuzzi, Giorgio Pisani, Cosimo Politecn Milan Dept Energy I-20156 Milan Italy Terna Rete Italia SpA I-00156 Rome Italy

As renewable energy sources are integrated into power systems, the complexity of their management increases. For the analysis of modern power systems, clustering algorithms have become indispensable tools. The representation of an electric power transmission system involves conceptualizing it as a graph-a mathematical structure depicting a set of nodes and edges. In the context of power systems, these nodes correspond to buses, and the edges represent branches. Weighting may be assigned to the edges to denote the strength of their connections. In this paper, spectral clustering and hierarchical agglomerative clustering have been employed to analyze the Italian transmission network. Spectral clustering relies on the eigenvalues and eigenvectors derived from the Laplacian matrix associated with the network. The hierarchical agglomerative clustering, based on Th & eacute;venin impedance to define the distances, merges the two closest clusters at each step. The primary objective of this research is to identify distinct areas within the network based solely on its topology. In pursuit of this goal, admittances and Th & eacute;venin impedances have been employed as weights in the graph representation. By utilizing these approaches, the research aims at uncovering meaningful structures and patterns within the power transmission system based on the inherent connectivity and strength of the network elements. The techniques have been used on two real network models to demonstrate the performance of the algorithms with different grid configurations.

关键词： clustering algorithms Laplace equations Eigenvalues and eigenfunctions Europe Admittance Voltage control Renewable energy sources Hierarchical systems clustering techniques spectral clustering hierarchical clustering power grids

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Unsupervised Cross-View Subspace clustering via Adaptive Contrastive Learning

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IEEE TRANSACTIONS ON BIG DATA 2024年第5期10卷 609-619页

作者： Zhang, Zihao Wang, Qianqian Gao, Quanxue Pei, Chengquan Feng, Wei Xidian Univ Sch Telecommun Engn Xian 710071 Peoples R China Southeast Univ State Key Lab Measurement & Control Complex Syst E Nanjing 210018 Peoples R China Xidian Univ Sch Telecommun Engn Minist Educ Xian 710071 Peoples R China Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian 710049 Peoples R China

Cross-view subspace clustering has become a popular unsupervised method for cross-view data analysis because it can extract both the consistent and complementary features of data for different views. Nonetheless, existing methods usually ignore the discriminative features due to a lack of label supervision, which limits its further improvement in clustering performance. To address this issue, we design a novel model that leverages the self-supervision information embedded in the data itself by combining contrastive learning and self-expression learning, i.e., unsupervised cross-view subspace clustering via adaptive contrastive learning (CVCL). Specifically, CVCL employs an encoder to learn a latent subspace from the cross-view data and convert it to a consistent subspace with a self-expression layer. In this way, contrastive learning helps to provide more discriminative features for the self-expression learning layer, and the self-expression learning layer in turn supervises contrastive learning. Besides, CVCL adaptively chooses positive and negative samples for contrastive learning to reduce the noisy impact of improper negative sample pairs. Ultimately, the decoder is designed for reconstruction tasks, operating on the output of the self-expressive layer, and strives to faithfully restore the original data as much as possible, ensuring that the encoded features are potentially effective. Extensive experiments conducted across multiple cross-view datasets showcase the exceptional performance and superiority of our model.

关键词： Self-supervised learning Feature extraction clustering methods Correlation clustering algorithms Task analysis Semantics Contrastive learning cross-view clustering subspace clustering

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Eigenvalue Ratio Inspired Partition Learning and Fusion for Multiple Kernel clustering

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第12期36卷 8135-8147页

作者： Yang, Wenqi Tang, Chang Zheng, Xiao Zhu, Xinzhong Liu, Xinwang China Univ Geosci Sch Comp Sci Wuhan 430079 Peoples R China Natl Univ Def Technol Sch Comp Changsha 410073 Peoples R China Zhejiang Normal Univ Sch Comp Sci & Technol Jinhua 321004 Peoples R China Beijing Geekplus Technol Co Ltd AI Res Inst Beijing 100101 Peoples R China

Multiple kernel clustering (MKC) aims to extract and integrate the clustering information from a set of pre-defined kernels for handling data which cannot be linearly separated well. More precisely, existing MKC methods generally devote to learn the complementary information from a set of kernel partitions, whose feature dimensions are commonly fixed as the upper bound n or lower bound c , where n and c represents the number of samples and clusters, respectively. However, the adopting of the lower bound or upper bound generally leads to poor clustering performance caused by the lack or redundancy of clustering information carried by kernel partitions. To tackle this issue, we propose a novel late fusion multiple kernel clustering method, termed as Eigenvalue Ratio Inspired Partition Learning and Fusion for Multiple Kernel clustering (ERMKC), in this paper. Specifically, we propose an eigenvalue ratio based criterion to guide the kernel partition learning for each single kernel matrix, which ensures more suitable feature dimensions for the learnt kernel partitions. In addition, we also propose a novel late fusion model for fusing the learnt kernel partitions optimally. Furthermore, we conduct extensive experiments on numerous benchmark datasets to evaluate the proposed ERMKC method, whose results verify the effectiveness and advantage of the proposed method compared to the other state-of-the-art methods.

关键词： Kernel Eigenvalues and eigenfunctions clustering algorithms Partitioning algorithms clustering methods Measurement Mathematical models clustering multiple kernel clustering eigenvalue ratio late fusion strategy

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