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Bilateral Fast Low-Rank Representation With Equivalent Transformation for Subspace clustering

IEEE TRANSACTIONS ON MULTIMEDIA 2023年 25卷 6371-6383页

作者： Shen, Qiangqiang Yi, Shuangyan Liang, Yongsheng Chen, Yongyong Liu, Wei Harbin Inst Technol Shenzhen Sch Elect & Informat Engn Shenzhen 518055 Guangdong Peoples R China Shenzhen Inst Informat Technol Sch Software Engn Shenzhen 518055 Guangdong Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Shenzhen 518055 Peoples R China Harbin Inst Technol Guangdong Prov Key Lab Novel Secur Intelligence T Shenzhen 518055 Guangdong Peoples R China Harbin Inst Technol Shenzhen Inst Informat Technol Shenzhen 518055 Guangdong Peoples R China

In recent years, low-rank representation (LRR) has received increasing attention on subspace clustering. Due to inevitable matrix inversion and singular value decomposition in each iteration, however, most of existing LRR algorithms may suffer from high computational complexity, and hence can not cope with the large-scale sample data commendably. To overcome this problem, in this paper, we propose a bilateral fast low-rank representation (BFLRR), which has a linear time complexity with respect to the number of samples. Specifically, we introduce the equivalent transformation method to remove the null spaces of both the columns and rows of the coefficient matrix so that a hypercompact coefficient matrix can be learned. Furthermore, the proposed BFLRR is embedded into a distributed framework as DFC-BFLRR to make it more efficient, which utilizes a combination of the global and local projection matrices. Extensive experiments are carried out on real datasets, and the results testify that the proposed methods not only perform faster-computing speed but also obtain favorable clustering accuracy in comparison with the competing methods among large-scale sample data.

关键词： Null space Sparse matrices Streaming media Singular value decomposition Minimization Matrix decomposition clustering algorithms Distribute equivalent transformation fast low-rank subspace clustering

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A clustering Algorithm for Polygonal Data Applied to Scientific Journal Profiles

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第11期45卷 13766-13777页

作者： Silva, Wagner J. F. Souza, Pedro J. C. Souza, Renata M. C. R. Cysneiros, Francisco Jose A. Univ Fed Pernambuco Informat Ctr Recife PE Brazil Univ Fed Pernambuco Dept Estatst Recife Brazil

Millions of papers are submitted and published every year, but researchers often do not have much information about the journals that interest them. In this paper, we introduced the first dynamical clustering algorithm for symbolic polygonal data and this was applied to build scientific journals profiles. Dynamic clustering algorithms are a family of iterative two-step relocation algorithms involving the construction of clusters at each iteration and the identification of a suitable representation or prototype (means, axes, probability laws, groups of elements, etc.) for each cluster by locally optimizing an adequacy criterion that measures the fitting between clusters and their corresponding prototypes The application gives a powerful vision to understand the main variables that describe journals. Symbolic polygonal data can represent summarized extensive datasets taking into account variability. In addition, we developed cluster and partition interpretation indices for polygonal data that have the ability to extract insights about clustering results. From these indices, we discovered, e.g., that the number of difficult words in abstract is fundamental to building journal profiles.

关键词： clustering algorithms Heuristic algorithms Prototypes Partitioning algorithms clustering methods Text mining Data science Dynamical clustering algorithm polygonal data scientific journal profiles symbolic data analysis

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Exploring Human Mobility Correlations Using Semisupervised Hypergraph clustering

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2025年

作者： Wang, Ming Zhang, Yong Zhao, Xia Wei, Xiulan Yin, Baocai Beijing Univ Technol Beijing Inst Artificial Intelligence Fac Informat Technol Beijing Key Lab Multimedia & Intelligent Software Beijing 100124 Peoples R China Beijing Univ Civil Engn & Architecture Beijing Key Lab Gen Aviat Technol Beijing 102616 Peoples R China

Bound by social bonds, people often travel in groups, exhibiting mutual correlations in their mobility behaviors, known as mobility correlations. However, conventional graphs, which describe pairwise connections between vertices, fail to adequately capture the complex many-to-many connection characteristics of multiple individuals. To address this limitation, we propose a novel semisupervised hypergraph clustering (SSHC) model aimed at unraveling the intricate correlations among individuals before discerning their latent clusters. Each individual is characterized by features that encompass spatial, temporal, and attributive diversity. The high-order correlations concealed among multiple individuals are captured using a hypergraph framework featuring hyperedges. Building upon this foundation, we fuse individuals' latent feature representations, extracted via an auto-encoder, with their structural representations derived from a hypergraph convolutional network. Through this fusion, we identify clusters of similar individuals exhibiting mobility correlations. Notably, our model incorporates a semisupervised learning scheme, leveraging priori patterns gleaned from a small pool of ground-truth data to better express latent patterns within unlabeled datasets. Experimental validations conducted in five public datasets corroborate the superiority of our proposed model over baseline methods in elucidating complex correlations within vast datasets. In addition, experiments conducted on Beijing Transit Smart Card Data showcase promising results in identifying correlated individuals with similar mobility patterns. The delineation of groups of individuals exhibiting mobility correlations is of paramount importance, particularly in accurately detecting their connection networks during public emergencies, such as the transmission of dangerous pathogens.

关键词： Correlation clustering algorithms Feature extraction Graph neural networks Vectors Pairwise error probability Deep learning Data models clustering methods Big data in transportation human mobility patterns hypergraph clustering hypergraph convolutional network semisupervised learning semisupervised learning

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TC-DTW: Accelerating multivariate dynamic time warping through triangle inequality and point clustering

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INFORMATION SCIENCES 2023年 621卷 611-626页

作者： Shen, Daniel S. Chi, Min Stanford Univ 450 Serra Mall Stanford CA 94305 USA North Carolina State Univ 890 Oval Dr Raleigh NC 27695 USA

Dynamic time warping (DTW) plays an important role in analytics on time series. Despite the large body of research on speeding up univariate DTW, the method for multivariate DTW has not been improved much in the last two decades. The most popular algorithm used today is still the one developed nineteen years ago. This paper presents a solution that, as far as we know, for the first time consistently outperforms the classic multivariate DTW algorithm across dataset sizes, series lengths, data dimensions, temporal window sizes, and machines. The new solution, named TC-DTW, introduces triangle inequality and point clustering into the algorithm design on lower bound calculations for multivariate DTW. In experiments on DTW-based nearest neighbor finding, the new solution avoids as much as 98% (60% average) DTW distance calculations and yields as much as 25x (7.5x average) speedups. (C) 2022 Elsevier Inc. All rights reserved.

关键词： clustering algorithms

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Random Feature-Based Collaborative Kernel Fuzzy clustering for Distributed Peer-to-Peer Networks

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2023年第2期31卷 692-706页

作者： Wang, Yingxu Han, Shiyuan Zhou, Jin Chen, Long Chen, C. L. Philip Zhang, Tong Liu, Zhulin Wang, Lin Chen, Yuehui Univ Jinan Shandong Prov Key Lab Network Based Intelligent Co Jinan 250022 Peoples R China Univ Macau Fac Sci & Technol Dept Comp & Informat Sci Macau 999078 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510641 Peoples R China

Kernel clustering has the ability to get the inherent nonlinear structure of the data. But the high computational complexity and the unknown representation of the kernel space make it unavailable for the data clustering in distributed peer-to-peer (P2P) networks. To solve this issue, we propose a new series of random feature-based collaborative kernel clustering algorithms in this article. In the most basic algorithm, each node in a distributed P2P network first maps its data into a low-dimensional random feature space with the approximation of the given kernel by using the random Fourier feature mapping method. Then, each node independently searches the clusters with its local data and the collaborative knowledge from its neighbor nodes, and the distributed clustering is performed among all network nodes until reaching the global consensus result, i.e., all nodes have the same cluster centers. In addition, an improved version is designed with assignment of feature weights, which is optimized by the maximum-entropy technique to extract important features for the cluster identification. What's more, to relief the impact of different kernel functions and related parameters on clustering results, the combination of multiple kernels rather than a single kernel is adopted for the low-dimensional approximation, and the optimized weights are assigned to provide the guidance on the choice of the kernels and their parameters and discover significant features at the same time. Experiments on synthetic and real-world datasets show that the proposed methods achieve similar and even better results than the traditional kernel clustering methods on various performance metrics, including the average classification rate, the average normalized mutual information, and the average adjusted rand index. More importantly, the low-dimensional random features approximated to kernels and the distributed clustering mechanism adopted in these methods bring the greatly lower temporal complexit

关键词： Kernel Distributed databases Peer-to-peer computing Collaboration clustering algorithms clustering methods Prototypes Collaborative distributed clustering feature weights kernel fuzzy clustering random Fourier feature

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The Design of clustering Algorithm and MAC Protocol for Low Delay Underwater Acoustic Sensor Networks

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IEEE SENSORS JOURNAL 2023年第3期23卷 3251-3261页

作者： Zhang, Qinzheng Wang, Haiyan Zhao, Ruiqin Shen, Xiaohong He, Ke Zhang, Hongwei Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China Shaanxi Univ Sci & Technol Sch Elect Informat & Artificial Intelligence Xian 710021 Peoples R China

Underwater acoustic sensor networks (UASNs), which are popular in various application fields, including marine resources development, environmental exploration, seismic monitoring, etc., have made great progress in recent years. To maintain good scheduling performance, clustering algorithms and multiple access control (MAC) protocols have been widely used in sensor networks to improve network efficiency. However, the existing algorithms and protocols still have many shortcomings. For example, many clustering algorithms consider the delay performance little, the cluster structures are not always fully utilized by MAC protocols, and the cluster maintenance strategies are not considered. This article is devoted to solving those problems. By taking the node traffic and distances into account simultaneously, we design the cluster structure reasonably. And based on this structure, we plan a conflict-free handshake protocol with minimal idle time gaps. Besides, we also design a joining-cluster strategy for the free nodes to maintain the network without interference. Simulation results show that our work can perform well in network uniformity and end-to-end delay.

关键词： Delays clustering algorithms Sensors Protocols Media Access Protocol Underwater acoustics Mathematical models clustering algorithm low delay multiple access control (MAC) protocol underwater acoustic sensor network (UASN)

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Transfer Learning-Based Collaborative Multiview clustering

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2023年第4期31卷 1163-1177页

作者： Liu, Xiangdao Wang, Rongrong Zhou, Jin Chen, C. L. Philip Zhang, Tong Chen, Yuehui Han, Shiyuan Du, Tao Ji, Ke Zhang, Kun Univ Jinan Shandong Prov Key Lab Network Based Intelligent Co Jinan 250022 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510641 Peoples R China

Collaborative multiview clustering methods can efficiently realize the view fusion by exploring complementary and consistent information among multiple views. However, these studies ignore all the differences between multiple views in fusion. In fact, in the multiview clustering, the data are diverse from view to view. The larger the difference between any two views is, the more the fusion of these views is required. Moreover, a global tradeoff parameter is generally adopted to restrain the penalty related to the disagreement of all views, which is often defined empirically. Inspired by the idea of transfer learning, a series of novel collaborative multiview clustering algorithms are proposed to tackle these challenges. In the most basic one, each view performs clustering independently and learns from others to improve its own clustering performance, in which a global learning factor is defined to control the interaction between multiple views. The fuzzy memberships are regarded as the important knowledge to provide guidance between views, and the consensus constraint is defined to ensure the consistent partitions of all views. In addition, the local adaptive learning factors between any two views instead of a global fixed one are adopted in an improved version to emphasize the difference between views, and the adjustment strategy for the learning factor is further designed to guarantee the stability of multiview clustering without the influence of initial values. Finally, to identify the significance of different views to the clustering, the extended versions are excavated with the assignment of view weights and the maximum entropy regularization technique is employed to optimize the weights. Experiments on various real-world multiview datasets verify the superiority of the presented approaches.

关键词： Collaboration clustering algorithms Transfer learning Partitioning algorithms clustering methods Entropy Adaptive learning Adaptive learning factor adjustment collabo- rative multiview clustering entropy regularization transfer clustering

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Computational-Intelligence-Inspired Adaptive Opportunistic clustering Approach for Industrial IoT Networks

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IEEE INTERNET OF THINGS JOURNAL 2023年第9期10卷 7884-7892页

作者： Chithaluru, Premkumar Al-Turjman, Fadi Kumar, Manoj Stephan, Thompson Chaitanya Bharathi Inst Technol Dept Comp Sci & Engn Hyderabad 500075 India Univ Kyrenia Fac Engn Res Ctr AI & IoT TR-99320 Kyrenia Turkiye Near East Univ AI & Robot Inst Artificial Intelligence Engn Dept 1403 Nicosia Istanbul Turkiye Univ Wollongong Dubai Fac Engn & Informat Sci Dubai U Arab Emirates M S Ramaiah Univ Appl Sci Dept Comp Sci & Engn Bengaluru 560054 India

The major issues and challenges of the Industrial Internet of Things (IIoT) include network resource management, self-organization;routing, mobility, scalability, security, and data aggregation. Resource management in IIoT is a challenging issue, starting from the deployment and design of sensor nodes, networking at cross-layer, networking software development, application types, environmental conditions, monitoring user decisions, querying process, etc. In this article, computational intelligence (CI) and its computing, such as neural networks and fuzzy logic, are used to tackle the challenges of resource management in the IIoT. The incorporation of the neuro-fuzzy technique into the IIoT contributes to the self-managing intelligence systems' self-organizing and self-sustaining capabilities, offering real-time computations and services in a pervasive networking environment. Most of the problems in IIoT are real-time based;they require fast computation, real-time optimal solutions, and the need to be adaptive to the situation of the events and data traffic to achieve the desired goals. Hence, neural networks and fuzzy sets would form appropriate candidates for implementing most of the computations involved in the issues of resource management in IIoT networks. A real-time testbed network is simulated and implemented on the Crossbow mote (sensor node) using TinyOS.

关键词： Industrial Internet of Things Peer-to-peer computing Real-time systems Routing Resource management Quality of service clustering algorithms Computational intelligence (CI) industrial IoT mobile node neuro-fuzzy technique resource management self-managing self-organizing self-sustaining

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Variational Bayesian Inference clustering-Based Joint User Activity and Data Detection for Grant-Free Random Access in mMTC

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IEEE INTERNET OF THINGS JOURNAL 2023年第11期10卷 9906-9916页

作者： Zhang, Zhaoji Guo, Qinghua Li, Ying Jin, Ming Huang, Chongwen Xidian Univ Sch Telecommun Engn Xian 710071 Peoples R China Univ Wollongong Sch Elect Comp & Telecommun Engn Wollongong NSW 2522 Australia Ningbo Univ Fac Elect Engn & Comp Sci Ningbo 315211 Peoples R China Zhejiang Univ Coll Informat Sci & Elect Engn 310027 Hangzhou Peoples R China Zhejiang Univ Zhejiang Prov Key Lab Informat Proc Commun & Netwo Hangzhou 310027 Peoples R China Zhejiang Univ Int Joint Innovat Ctr Haining 314400 Peoples R China

Tailor-made for massive connectivity and sporadic access, grant-free random access has become a promising candidate access protocol for massive machine-type communications (mMTC). Compared with conventional grant-based protocols, grant-free random access skips the exchange of scheduling information to reduce the signaling overhead, and facilitates the sharing of access resources to enhance access efficiency. However, some challenges remain to be addressed in the receiver design, such as the unknown identity of active users and multiuser interference (MUI) on shared access resources. In this work, we deal with the problem of joint user activity and data detection for grant-free random access. Specifically, the approximate message passing (AMP) algorithm is first employed to mitigate MUI and decouple the signals of different users. Then, we extend the data symbol alphabet to incorporate the null symbols from inactive users. In this way, the joint user activity and data detection problem is formulated as a clustering problem under the Gaussian mixture model. Furthermore, in conjunction with the AMP algorithm, a variational Bayesian inference-based clustering (VBIC) algorithm is developed to solve this clustering problem. Simulation results show that, compared with state-of-art solutions, the proposed AMP-combined VBIC (AMP-VBIC) algorithm achieves a significant performance gain in detection accuracy.

关键词： Symbols clustering algorithms Inference algorithms Multiuser detection Bayes methods Matching pursuit algorithms Internet of Things Approximate message passing (AMP) clustering grant-free joint user activity and data detection massive machine-type communications (mMTC) variational Bayesian inference (VBI)

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A Sparse Framework for Robust Possibilistic K-Subspace clustering

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2023年第4期31卷 1124-1138页

作者： Zeng, Shan Duan, Xiangjun Li, Hao Bai, Jun Tang, Yuanyan Wang, Zhiyong Wuhan Polytech Univ Coll Math & Comp Sci Wuhan Peoples R China Deakin Univ Sch Informat Technol Melbourne Vic Australia Univ Macau Fac Sci & Technol Macau Peoples R China Univ Sydney Sch Comp Sci Sydney NSW Australia

clustering noisy, high-dimensional, and structurally complex data have always been a challenging task. As most existing clustering methods are not able to deal with both the adverse impact of noisy samples and the complex structures of data, in this article, we propose a novel robust and sparse possibilistic K-subspace (RSPKS) clustering algorithm to integrate subspace recovery and possibilistic clustering algorithms under a unified sparse framework. First, the proposed method sparsifies the membership matrix and the subspace projection vector under a dual-sparse framework to handle high-dimensional noisy data. This unifies dimensionality reduction and clustering using one objective function for which the optimization can be realized through synchronous iteration. Second, the reconstruction error of each sample in the local subspace is used as the distance metric for classification. That is, each sample itself is treated as a clustering prototype so as not to be affected by the structure of the overall data distribution. Therefore, the clustering prototype construction problem of the data with complex structures can be better addressed. Finally, to deal with nonlinear regions, our RSPKS method is further extended into a kernelized version, namely the kernelized RSPKS clustering algorithm. The experimental results on both synthetic and real-world datasets demonstrate that our proposed method outperforms state-of-the-art algorithms in terms of clustering accuracy.

关键词： clustering algorithms Partitioning algorithms Prototypes clustering methods Sparse matrices Noise measurement Power capacitors Dual-sparse framework local subspace possi- bilistic K-subspace (KSS) clustering

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