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Feature Space Recovery for Efficient Incomplete Multi-View clustering

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第9期36卷 4664-4677页

作者： Long, Zhen Zhu, Ce Comon, Pierre Ren, Yazhou Liu, Yipeng Univ Elect Sci & Technol China Sch Commun & Informat Engn Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China Univ Grenoble Alpes GIPSA Lab CNRS Grenoble INP F-38000 Grenoble France

The tensor singular value decomposition (t-SVD) based incomplete multi-view clustering (IMVC) has received wide attention due to its ability to capture high-order correlations. However, t-SVD suffers from rotation sensitivity, failing to fully explore both inter- and intra-view consistencies. Besides, current methods mainly consider inter- or intra-view correlations, ignoring the low-rank information of sample features within views. To address these weaknesses, we first propose a feature space recovery based IMVC (FSR-IMVC) method, where low-rank feature space recovery and low-rank tensor ring based consistency learning are considered into a unified framework. Furthermore, we extend FSR-IMVC by incorporating anchor learning on the latent feature space, resulting in a scalable FSR-IMVC (sFSR-IMVC) approach that is well-suited to large-scale data. In an iterative way, the learned inter- and intra-view correlations will guide the recovery of missing features, while the explored low-rank information from feature spaces will in turn facilitate consistency exploration, eventually achieving outstanding clustering performance. Experimental results show that FSR-IMVC provides a significant improvement over known state-of-the-art algorithms in terms of ACC, NMI and Purity. Compared with FSR-IMVC, sFSR-IMVC performs slightly worse in clustering accuracy, but offers a notable advantage in computational efficiency, particularly for large-scale datasets.

关键词： Tensors Correlation Task analysis clustering algorithms Space exploration Social networking (online) Feature extraction Anchor learning feature space recovery incomplete multi-view clustering low-rank tensor ring approximation

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Learning Idempotent Representation for Subspace clustering

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第3期36卷 1183-1197页

作者： Wei, Lai Liu, Shiteng Zhou, Rigui Zhu, Changming Liu, Jin Shanghai Maritime Univ Coll Informat Engn Shanghai 201306 Peoples R China

The critical point for the success of spectral-type subspace clustering algorithms is to seek reconstruction coefficient matrices that can faithfully reveal the subspace structures of data sets. An ideal reconstruction coefficient matrix should have two properties: 1) it is block-diagonal with each block indicating a subspace;2) each block is fully connected. We find that a normalized membership matrix naturally satisfies the above two conditions. Therefore, in this paper, we devise an idempotent representation (IDR) algorithm to pursue reconstruction coefficient matrices approximating normalized membership matrices. IDR designs a new idempotent constraint. And by combining the doubly stochastic constraints, the coefficient matrices which are close to normalized membership matrices could be directly achieved. We present an optimization algorithm for solving IDR problem and analyze its computation burden as well as convergence. The comparisons between IDR and related algorithms show the superiority of IDR. Plentiful experiments conducted on both synthetic and real-world datasets prove that IDR is an effective subspace clustering algorithm.

关键词： clustering algorithms Sparse matrices Optimization Laplace equations Synthetic data Convergence Computational modeling Subspace clustering idempotent matrix doubly stochastic constraint normalized membership matrix

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Spatial clustering Approach for Vessel Path Identification

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IEEE ACCESS 2024年 12卷 66248-66258页

作者： Abuella, Mohamed Atoui, M. Amine Nowaczyk, Slawomir Johansson, Simon Faghani, Ethan Halmstad Univ Ctr Appl Intelligent Syst Res CAISR S-30118 Halmstad Sweden Cetasol AB S-41251 Gothenburg Sweden

This paper addresses the challenge of identifying the paths for vessels with operating routes of repetitive paths, partially repetitive paths, and new paths. We propose a spatial clustering approach for labeling the vessel paths by using only position information. We develop a path clustering framework employing two methods: a distance-based path modeling and a likelihood estimation method. The former enhances the accuracy of path clustering through the integration of unsupervised machine learning techniques, while the latter focuses on likelihood-based path modeling and introduces segmentation for a more detailed analysis. The result findings highlight the superior performance and efficiency of the developed approach, as both methods for clustering vessel paths into five clusters achieve a perfect F1-score. The approach aims to offer valuable insights for route planning, ultimately contributing to improving safety and efficiency in maritime transportation.

关键词： Trajectory Navigation Heuristic algorithms Seaports Marine vehicles clustering algorithms Feature extraction Spatial databases Maritime communications Nearest neighbor methods Spatial clustering vessel path identification maritime transportation average nearest neighbor distance hierarchical clustering likelihood estimation

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Tensor-Based Possibilistic C-Means clustering

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第10期32卷 5939-5950页

作者： Benjamin, Josephine Bernadette M. Yang, Miin-Shen Univ Santo Tomas Dept Math & Phys Manila 1008 Philippines Chung Yuan Christian Univ Dept Appl Math Taoyuan 32023 Taiwan

The current data acquisition techniques enable the gathering and storage of extensive datasets, encompassing multidimensional arrays. Recent researchers focus on the analysis of large datasets having diverse data points. These multidimensional datasets comprise diverse data points and can be represented as tensors or multidimensional arrays. clustering, a data analysis technique, can be used to discover and reveal latent data patterns from these datasets. The traditional clustering algorithms such as k-means, fuzzy c-means (FCM), and possibilistic c-means (PCM) pose some drawbacks in efficiently delivering high-quality clustering results for tensor or multidimensional array data. This may stem from the fact that these algorithms are primarily designed for single-view or low-array datasets, rendering them less suitable for the complexities of multidimensional arrays. In response to this challenge, this article introduces the tensor-based PCM (TPCM) algorithm. TPCM utilizes a tensor distance (TD) function as the distance metric, different from the usual Euclidean distance. The TD function evaluates the distance between data points and cluster centers by considering relationships among different coordinates. To further enhance the analysis, the canonical polyadic decomposition (CPD) method and PARAFAC2 decomposition techniques are used to restructure heterogeneous data into low-order tensors. Our experiments consider two types of datasets: multiview datasets and tensor datasets. CPD is applied for tensor data decomposition, while PARAFAC2, a CPD variant, addresses multiview data with varying feature space sizes in each view. Through comprehensive illustrations and evaluations using synthetic and real datasets, we demonstrate the superior performance of TPCM. Experimental results reveal that TPCM consistently achieves higher clustering performance compared to most existing clustering algorithms.

关键词： Tensors clustering algorithms Phase change materials Arrays Linear programming Heuristic algorithms Euclidean distance clustering possibilistic C-means (PCM) tensor data tensor decomposition tensor distance (TD) tensor-based clustering tensor-based PCM (TPCM)

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An Overall Framework of Modeling, clustering, and Evaluation for Trapezoidal Information Granules

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第6期32卷 3484-3496页

作者： Tang, Yiming Gao, Jianwei Pedrycz, Witold Xi, Lei Ren, Fuji Hefei Univ Technol Sch Comp & Informat AnHui Prov Key Lab Affect Comp & Adv Intelligent M Hefei 230601 Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6R 2V4 Canada Hefei Univ Technol Sch Comp & Informat Hefei 230601 Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 610056 Peoples R China Polish Acad Sci Syst Res Inst PL-00901 Warsaw Poland Istinye Univ Res Ctr Performance & Prod Anal TR-34010 Istanbul Turkiye

In existing granular clustering algorithms, the design of coverage and specificity does not fully capture the inherent structural characteristics of granular data together with the optimization issue, and the current weight setting for the granular data is not sufficient. To address these problems, in this study, the trapezoidal information granule, which is rarely studied before, is concentrated, and we come up with a novel granular clustering algorithm called the weighted possibilistic fuzzy c-means algorithm for trapezoidal granularity (WPFCM-T). First, under the acknowledged principle of justifiable granularity, novel functions of coverage and specificity are designed for trapezoidal information granules, considering the internal characteristics of such granules. The idea of particle swarm optimization (PSO) is exploited to upgrade the established granular data, and then the trapezoidal information granule construction (TIGC) method is proposed to realize granular modeling. Second, an exponential weight is constructed with regard to coverage and specificity, while a novel distance via $\alpha$-cuts is given. The possibilistic fuzzy c-means structure is introduced into granular clustering, in which the new weight and distance are integrated, resulting in the proposed WPFCM-T algorithm. Third, the RC is studied to evaluate granular clustering, and hence an overall framework including granular modeling, clustering, and evaluation is constructed. Finally, through experiments completed on artificial datasets, UCI datasets, large datasets, high-dimensional datasets, and noisy datasets, WPFCM-T has superior granular data reconstruction ability by contrast with other granular clustering algorithms, indicating that the granular clustering performance of WPFCM-T is better than the others.

关键词： clustering algorithms Fuzzy sets Data models Numerical models Heuristic algorithms Optimization Granular computing Fuzzy clustering granular computing principle of justifiable granularity trapezoidal information granule

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A clustering and Routing Algorithm for Fast Changes of Large-Scale WSN in IoT

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IEEE INTERNET OF THINGS JOURNAL 2024年第3期11卷 5036-5049页

作者： Fan, Bing Xin, Yanan North China Elect Power Univ Sch Elect & Elect Engn Beijing 102206 Peoples R China State Grid Xinjiang Elect Power Co Ltd Elect Power Res Inst Urumqi 830011 Xinjiang Peoples R China

With the rapid development of Internet of Things (IoT) technology, the number of sensors in IoT is proliferating and the monitoring range is expanding. Wireless sensor networks (WSNs) in IoT are large-scale WSNs with fast changes in node energy due to long-distance communication, and node distribution due to the death of nodes. A clustering and routing algorithm for the fast changes (FC-CRAs) of large-scale WSNs in IoT is proposed. In the FC-CRA, the clusters are constructed according to the cluster radius which can dynamically adapt to the change in node energy and distribution. The intracluster routing is based on the path energy function to save and balance node energy. The intercluster routing is performed in an intercluster communication node set to avoid the premature death of near-BS nodes and ensure the continuity of data transmission. Compared with the other algorithms, the FC-CRA can improve the node lifetime, network throughput, and save the network energy in large-scale or sparse WSNs. The proposed algorithm can alleviate the "energy hole" problem and improve the reliability of data transmission, which is very important for some IoT application scenes with wide-area monitoring requirement and harsh or hazardous situations.

关键词： clustering algorithms Wireless sensor networks Heuristic algorithms Internet of Things Routing Sensors Energy consumption clustering and routing algorithm energy balance energy hole Internet of Things (IoT) large-scale wireless sensor network (WSN)

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ReShape: A Universal Data Processing Approach for clustering Inhomogeneous and Weakly Separated Manifold Data

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第9期20卷 10901-10911页

作者： Shang, Zhongju Dang, Yaoguo Zhu, Jun Wang, Haowei Wang, Junjie Nanjing Univ Aeronaut & Astronaut Coll Econ & Management Nanjing 210016 Peoples R China Northwestern Polytech Univ Sch Civil Aviat Xian 710060 Peoples R China Northwestern Polytech Univ Yangtze River Delta Res Inst Suzhou 215400 Peoples R China Rice Rick Digitalizat PTE Ltd Singapore 308900 Singapore

Classical clustering methods such as k-means, spectral clustering, DBSCAN, and DPC often operate under specific assumptions about data characteristics. However, the increasing diversity and complex characteristic of data, coupled with limited prior knowledge, hinder the direct application of these clustering algorithms. Existing data processing approaches, such as ReScale and CDF-TS, are capable of homogenizing cluster density, but tailor to density-based algorithms that does not perform well on weakly separated or manifold clusters. To overcome these issues, we propose a novel data processing method (ReShape) facilitating the direct application of well-known clustering algorithms without modifications. The ReShape integrates three key components: a homogenization factor for uniforming varying cluster densities, a separation factor for enhancing cluster separations, and a joint distance considering both global and local consistency to measure distances in manifold datasets. The proposed method is universally applicable, and can match implicit assumptions of existing clustering algorithms. Extensive experiments and a case study for fault diagnosis involving k-means, spectral clustering, DBSCAN, and DPC demonstrates the efficacy of the ReShape approach.

关键词： clustering algorithms Data processing distance measurement fault diagnosis

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Late Fusion Multiview clustering via Min-Max Optimization

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第7期35卷 9417-9427页

作者： Li, Miaomiao Liu, Xinwang Zhang, Yi Liang, Weixuan Changsha Univ Coll Elect Informat & Elect Engn Changsha Peoples R China Natl Univ Def Technol Coll Comp Changsha Peoples R China

Multiview clustering (MVC) sufficiently exploits the diverse and complementary information among different views to improve the clustering performance. As a representative algorithm of MVC, the newly proposed simple multiple kernel k-means (SimpleMKKM) algorithm takes a min-max formulation and applies a gradient descent algorithm to decrease the resultant objective function. It is empirically observed that its superiority is attributed to the novel min-max formulation and the new optimization. In this article, we propose to integrate the min-max learning paradigm adopted by SimpleMKKM into late fusion MVC (LF-MVC). This leads to a tri-level max-min-max optimization problem with respect to the perturbation matrices, weight coefficient, and clustering partition matrix. To solve this intractable max-min-max optimization problem, we design an efficient two-step alternative optimization strategy. Furthermore, we analyze the generalization clustering performance of the proposed algorithm from the theoretical perspective. Comprehensive experiments have been conducted to evaluate the proposed algorithm in terms of clustering accuracy (ACC), computation time, convergence, as well as the evolution of the learned consensus clustering matrix, clustering with different numbers of samples, and analysis of the learned kernel weight. The experimental results show that the proposed algorithm is able to significantly reduce the computation time and improve the clustering ACC when compared to several state-of-the-art LF-MVC algorithms.

关键词： clustering algorithms Optimization Kernel Partitioning algorithms Perturbation methods Minimax techniques Task analysis clustering ensemble multiple kernel clustering multiview clustering (MVC)

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Self-Supervised Spatiotemporal clustering of Vehicle Emissions With Graph Convolutional Network

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第11期35卷 16301-16312页

作者： Pei, Lihong Cao, Yang Kang, Yu Xu, Zhenyi Zhao, Zhenyi Univ Sci & Technol China Dept Automat Hefei 230026 Peoples R China Univ Sci & Technol China Inst Adv Technol Hefei 230088 Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei 230088 Peoples R China

Spatiotemporal clustering of vehicle emissions, which reveals the evolution pattern of air pollution from road traffic, is a challenging representation learning task due to the lack of supervision. Some recent work building upon graph convolutional network (GCN) models the intrinsic spatiotemporal correlations among the nodes in road networks as graph representations for clustering. However, these existing methods ignore the interactions between spatial and temporal variations in vehicle emissions, resulting in incomplete descriptions and inaccurate detection of the evolution pattern of air pollution. To address this issue, this article proposes a two-way self-supervised spatiotemporal representation learning scheme, in which the temporal and spatial features are progressively learned in a mutually reinforced manner. Our proposed method is based on the observation that though the variation in vehicle emissions in the road network is consistent in the spatial and temporal domains, its expression is more distinct in temporal sequences. To this end, the input emission data are first projected into an initial temporal representation space spanned by the captured features from a pretrained BiLSTM network. Then the generated distribution of temporal features is used to construct an objective constraint for high-purity clustering through a two-way self-supervised mechanism, which is leveraged as a constraint for the feature clustering of a GCN. Furthermore, to eliminate the initial errors, a joint optimization scheme is presented to generate the decoupled clustering results through the progressive refinement of representation and clustering. Our proposed method is evaluated on the traffic emission dataset of Xian city in 2020, and the experimental results have demonstrated the superiority against the state-of-the-art.

关键词： Spatiotemporal phenomena Roads Couplings Task analysis Feature extraction clustering algorithms Space vehicles Deep spatiotemporal clustering spatiotemporal coupling spatiotemporal evolution pattern detection vehicle emissions' distribution

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Power Attributed Graph Embedding and clustering

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第1期35卷 1439-1444页

作者： Labiod, Lazhar Nadif, Mohamed Univ Paris Cite Ctr Borelli UMR 9010 F-75006 Paris France

Representation learning is a central problem of attributed networks (ANs) data analysis in a variety of fields. Given an attributed graph, the objectives are to obtain a representation of nodes and a partition of the set of nodes. Usually, these two objectives are pursued separately via two tasks that are performed sequentially, and any benefit that may be obtained by performing them simultaneously is lost. In this brief, we propose a power-attributed graph embedding and clustering (PAGEC for short) in which the two tasks, embedding and clustering, are considered together. To jointly encode data affinity between node links and attributes, we use a new powered proximity matrix. We formulate a new matrix decomposition model to obtain node representation and node clustering simultaneously. Theoretical analysis shows the close connections between the new proximity matrix and the random walk theory on a graph. Experimental results demonstrate that the PAGEC algorithm performs better, in terms of clustering and embedding, than state-of-the-art algorithms including deep learning methods designed for similar tasks in relation to attributed network datasets with different characteristics.

关键词： Task analysis clustering algorithms Matrix decomposition Representation learning Principal component analysis Optimization Learning systems Attributed graph clustering embedding spectral rotation

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