检索结果-内蒙古大学图书馆

Video Refereeing Model of Soccer Match Based on Fuzzy clustering and Cuckoo Optimization Algorithm

IEEE ACCESS 2024年 12卷 82536-82548页

作者： Wang, Ting Geng, Jinglong Wang, Jing Yan, Xiao Shanxi Univ Phys Educ Coll Taiyuan 030006 Peoples R China Taiyuan Normal Univ Phys Educ Coll Jinzhong 030619 Peoples R China Shanxi Vocat Univ Engn Sci & Technol Phys Educ Coll Jinzhong 030619 Peoples R China

Computer technology has rapidly advanced the use of computer-assisted refereeing in sports. However, motion image segmentation presents several challenges, including image blurring due to high-speed motion and the impact of ambient light on image accuracy. These challenges can affect the referee's judgment of the game. To address this issue, the research utilizes sinusoidal mapping and the simplex method to enhance the cuckoo algorithm. Additionally, the optimization accuracy is improved through sinusoidal adaptive discovery probability. To tackle the problem of the fuzzy clustering algorithm's weak global search ability, the clustering center is optimized by enhancing the cuckoo algorithm. To optimize the objective function, domain space information is introduced into the fuzzy clustering algorithm to address its sensitivity to noise. As the number of iterations approached 500, the experimental findings showed that the convergence accuracy of the particle swarm algorithm, the sparrow algorithm, the modified cuckoo algorithm, and the cuckoo algorithm were, respectively, 0.99, 0.89, 0.85, and 0.73. The enhanced algorithm model, enhanced cuckoo-fuzzy C-mean algorithm model, cuckoo-fuzzy C-mean algorithm model, and fuzzy C-mean algorithm model had delineation effects of 0.80, 0.72, 0.70, and 0.61, in that order. The outcomes show that the suggested algorithm can more accurately segment images from soccer matches. This provides a valuable reference for future computer-assisted match discrimination.

关键词： clustering algorithms Optimization Noise measurement Image segmentation Convergence Computational modeling Visualization Fuzzy systems fuzzy clustering cuckoo's algorithm match discrimination sine mapping

来源：评论

学校读者我要写书评

暂无评论

Unsupervised Deep Embedding for Fuzzy clustering

引用

IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第12期32卷 6744-6753页

作者： Zhang, Runxin Duan, Yu Nie, Feiping Wang, Rong Li, Xuelong Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect Xian 710072 Peoples R China Northwestern Polytech Univ Key Lab Intelligent Interact & Applicat Minist Ind & Informat Technol Xian 710072 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect Sch Comp Sci Xian 710072 Peoples R China China Telecom Corp Ltd Inst Artificial Intelligence TeleAI Beijing 100033 Peoples R China

Deep fuzzy clustering employs neural networks to discover the low-dimensional embedding space of data, providing an effective solution to the clustering problem posed by high-dimensional data. Although some algorithms have achieved good results in application, the field still faces the following problems: the lack of clustering loss function limits the development of deep clustering, and most of them use the self-training strategy-based Kullback-Leibler (KL) divergence;some algorithms directly use conventional constrained clustering objective function as the loss function in deep models, and update network parameters alternately, the optimization process is cumbersome. Focusing on the issues mentioned above, this article first proposed an unconstrained fuzzy $c$-means algorithm that can be solved using gradient descent and then used it as the clustering loss function to obtain a novel deep fuzzy clustering model named unsupervised deep embedding for fuzzy clustering. The proposed model simultaneously learns the low-dimensional representation of data and performs fuzzy clustering. It updates parameters through gradient descent and backpropagation, achieving end-to-end optimization. The proposed algorithm's effectiveness and competitiveness are fully demonstrated through extensive experiments conducted on image and text datasets.

关键词： clustering algorithms Optimization Linear programming Time complexity Fuzzy systems Decoding Reviews Autoencoder deep clustering gradient descent unconstrained fuzzy c -means

来源：评论

学校读者我要写书评

暂无评论

CC-GNN: A clustering Contrastive Learning Network for Graph Semi-Supervised Learning

引用

IEEE ACCESS 2024年 12卷 71956-71969页

作者： Qin, Peng Chen, Weifu Zhang, Min Li, Defang Feng, Guocan Sun Yat Sen Univ Sch Math Guangzhou 510275 Peoples R China Sun Yat sen Univ Guangdong Prov Key Lab Guangzhou 510275 Peoples R China Guangzhou Maritime Univ Coll Informat & Telecommun Engn Guangzhou 510725 Peoples R China Guangzhou Vocat Coll Technol & Business Guangzhou 511442 Peoples R China

In graph modeling, scarcity of labeled data is a challenging issue. To address this issue, state-of-the-art graph models learn the representation of graph data via contrastive learning. Those models usually use data augmentation techniques to generate positive pairs for contrastive learning, which aims to maximize the similarity of positive data pairs while minimizing the similarity of negative data pairs. However, samples with the same labels may be separately mapped in the feature space. To solve this problem, we introduce a novel model called clustering Contrastive Graph Neural Network (CC-GNN), which develops a new kind of grouped contrastive learning that maximizes the similarity of positive data groups and minimizes the similarity of negative groups. That is, contrastive learning is defined on a group level rather than on an instant level. We assert that parameters learned by this kind of contrastive learning will lead to better performance of graph neural networks for downstream classification tasks. We combined the clustering contrastive learning technique with three baseline GNN models for graph classification. We found that the performance of these models was significantly improved, which strongly supports our assertion. We also testified the models for node classification on three popular citation networks. Finally, we conducted an ablation study to analyze how the clustering contrastive learning influence the performance of a graph model.

关键词： Data augmentation Graph neural networks clustering algorithms Data models Semisupervised learning Analytical models Task analysis clustering contrastive learning graph data augmentation graph neural networks semi-supervised graph learning

来源：评论

学校读者我要写书评

暂无评论

Fuzzy-Based Deep Attributed Graph clustering

引用

IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第4期32卷 1951-1964页

作者： Yang, Yue Su, Xiaorui Zhao, Bowei Li, Guodong Hu, Pengwei Zhang, Jun Hu, Lun Chinese Acad Sci Xinjiang Tech Inst Phys & Chem Urumqi 830000 Peoples R China

Attributed graph (AG) clustering is a fundamental, yet challenging, task for studying underlying network structures. Recently, a variety of graph representation learning models has been proposed to effectively infer the node embeddings, which are then incorporated into conventional clustering techniques to identify meaningful clusters. While these models tend to preserve node proximities, which reflect the similarity between nodes in both structural and attribute dimensions, for representation learning, they generally overlook the crucial dependencies between node embeddings and the resulting clusters. To overcome this problem, we propose a novel fuzzy-based deep AG clustering model, namely FDAGC, which is capable of achieving the task in a purely unsupervised and end-to-end manner without additionally incorporating conventional clustering techniques. In particular, FDAGC first encodes network structures and node attributes into a compact representation with graph convolution. A reconstruction error is then estimated to minimize the information loss during network message-passing. Besides, we utilize a self-monitoring training strategy to optimize node embeddings, thus improving the cluster cohesion by guiding them toward cluster centers. In the training phase, our expectations about resulting clusters are explicitly incorporated into the optimization of FDAGC via the concept of fuzzy clustering, thus leading to more accurate clustering by coupling the dependency between graph representation learning and AG clustering. Extensive experiments have demonstrated the superior performance of FDAGC in terms of several evaluation metrics, such as accuracy, normalized mutual information, F1-score and adjusted rand index, on six real-world AGs with different scales.

关键词： clustering algorithms Task analysis Representation learning Training Optimization Convolution Computational modeling Attributed graph (AG) clustering fuzzy clustering graph convolution graph representation learning node embeddings

来源：评论

学校读者我要写书评

暂无评论

EvoS&R: Evolving Multiple Seeds and Radii for Varying Density Data clustering

引用

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第5期36卷 1964-1978页

作者： Chen, Jun-Xian Gong, Yue-Jiao Chen, Wei-Neng Zhang, Jun South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Hanyang Univ ERICA Sch Elect & Engn Ansan 15588 South Korea Nankai Univ Tianjin 300071 Peoples R China

Density clustering has shown advantages over other types of clustering methods for processing arbitrarily shaped datasets. In recent years, extensive research efforts has been made on the improvements of DBSCAN or the algorithms incorporating the concept of density peaks. However, these previous studies remain the problems of being sensitive to the parameter settings, and some of them will stuck in weak results when encountering the situations of varying-density distributions. To overcome these issues, we propose an evolution framework named EvoS&R that evolves multiple seeds and the corresponding radii for varying-density data clustering. Compared with the traditional methods, EvoS&R handles the parameter tuning and multi-density fitting problems in an integrated and straightforward manner. Note that, however, the underlying task in EvoS&R is a mixed-variable optimization problem that is challenging in nature. We specifically design a hybrid encoding differential evolution algorithm with novel encoding, mutation, etc., to solve the optimization problem efficiently. Extensive experiments on density-based datasets shows that our algorithm outperforms the other state-of-the-arts in most cases, which validates the effectiveness of the proposed method.

关键词： clustering algorithms Encoding Shape Tuning Optimization clustering methods Task analysis Density clustering varying density parameter tuning hybrid encoding differential evolution

来源：评论

学校读者我要写书评

暂无评论

Deep Denoising and clustering-Based Cooperative Spectrum Sensing for Non-Orthogonal Multiple Access

引用

IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2024年第5期10卷 1831-1842页

作者： Liao, Ningkang Zhang, Yongwei Wang, Yonghua Liu, Yang Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China South China Agr Univ Coll Math & Informat Guangzhou 510642 Peoples R China Guangdong Univ Technol Sch Informat Engn Guangzhou 510006 Peoples R China

Non-orthogonal multiple access (NOMA) technology offers higher communication throughput than its orthogonal multiple access counterpart. However, it also poses new challenges for spectrum sensing technology. Accurate spectrum sensing of a channel occupied by multiple users is challenging, especially in low signal-to-noise ratio environments. To improve the spectrum sensing performance, a spectrum sensing algorithm based on deep denoising and clustering is developed for power domain NOMA. First, a novel auto-encoder for deep denoising that can filter out the noise of signals is proposed. Then the auto-encoder is transplanted to a variational auto-encoder for extracting features with high separability. Finally, a ring K-means++ algorithm is proposed to classify features. In the experiments, simulations of algorithms are carried out in various scenarios with different numbers of primary users. The results show that the proposed algorithm outperforms other algorithms.

关键词： Sensors NOMA Noise reduction Feature extraction clustering algorithms Signal to noise ratio Training Spectrum sensing non-orthogonal multiple access deep denoising deep clustering

来源：评论

学校读者我要写书评

暂无评论

A Method to Enhance Web Service clustering by Integrating Label-Enhanced Functional Semantics and Service Collaboration

引用

IEEE ACCESS 2024年 12卷 61301-61311页

作者： Liu, Qingxue Wang, Lifang Du, Shengzhi Van Wyk, Barend Jacobus Kunming Univ Sch Mech & Elect Engn Kunming 650214 Peoples R China Tshwane Univ Technol Dept Elect Engn ZA-0001 Pretoria South Africa Tshwane Univ Technol Fac Engn & Built Environm ZA-0001 Pretoria South Africa

In Web service clustering, the service function vector (SFV) directly determines the quality of service (QoS) clustering. To improve service clustering performance, a method is proposed in this paper by integrating label-enhanced functional semantics and service collaboration. It improves the SFV from three aspects: generation model, corpus, and structural auxiliary information. At the generation model level, a Sentence-BERT is constructed based on singular value decomposition (SVD), to alleviate the anisotropy problem of BERT in vectorizing service descriptions. For corpus, the semantic features of SFV are supplemented by extracting specific named entities from service descriptions. Meanwhile, the service collaboration graph is established according to the collaboration relationship among Web services, which is conducive to the variational graph auto-encoders (VGAE) to realize service collaboration feature aggregation and further improve the SFV. Experiments show that the improved model, corpus and structural auxiliary information effectively enhance the SFV clustering. The proposed Web service clustering method is superior to the state-of-the-art methods.

关键词： Web services Semantics Collaboration Vectors Feature extraction clustering algorithms clustering methods Encoding Service collaboration service description service function vector (SFV) variational graph auto-encoders (VGAE) web service clustering

来源：评论

学校读者我要写书评

暂无评论

Concept Factorization Based Multiview clustering for Large-Scale Data

引用

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第11期36卷 5784-5796页

作者： Chen, Man-Sheng Wang, Chang-Dong Huang, Dong Lai, Jian-Huang Yu, Philip S. Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510275 Peoples R China Guangdong Prov Key Lab Intellectual Property & Big Guangzhou 510006 Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou 510006 Peoples R China South China Agr Univ Coll Math & Informat Guangzhou 510642 Peoples R China Guangdong Key Lab Informat Secur Technol Guangzhou 510006 Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou 510006 Peoples R China Univ Illinois Chicago IL 60607 USA

Most existing large-scale multiview clustering algorithms attempt to capture data distribution in multiple views by selecting view-wise anchor representations beforehand with k-means, or by direct matrix factorization on the original observations. Despite impressive performance, few of them have paid attention to the semantic correlations between anchor bases and cluster centroids, or even the underlying relations between clusters and data samples. In view of this, we propose a Concept Factorization based Multiview clustering for Large-scale Data (CFMC) method with nearly linear complexity. The anchor bases learning, coefficient expression with clear semantic cues and partitioning are integrated together in this unified model. Meanwhile, explicit connections among multiview data, anchor bases and clusters are modeled via coefficient representations with semantic meanings. A four-step alternate minimizing algorithm is designed to handle the optimization problem, which is proved to have linear time complexity w.r.t. the sample size. Extensive experiments conducted on several challenging large-scale datasets confirm the superiority of the method compared with the state-of-the-art methods.

关键词： Semantics clustering algorithms Data models Partitioning algorithms Correlation Tensors Representation learning Multiview clustering large-scale concept factorization semantic cues

来源：评论

学校读者我要写书评

暂无评论

Block-Diagonal Guided DBSCAN clustering

引用

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第11期36卷 5709-5722页

作者： Xing, Zheng Zhao, Weibing Shenzhen MSU BIT Univ Guangdong Lab Machine Percept & Intelligent Comp Shenzhen 518115 Peoples R China Chinese Univ Hong Kong Future Network Intelligence Inst Sch Sci & Engn Shenzhen 518172 Peoples R China

Cluster analysis constitutes a pivotal component of database mining, with DBSCAN being one of the most extensively employed algorithms in this domain. Nevertheless, DBSCAN is encumbered by several limitations, including challenges in processing high-dimensional datasets, a pronounced sensitivity to input parameters, and inconsistencies in generating reliable clustering outcomes. This paper presents a refined version of DBSCAN that utilizes the block-diagonal property of similarity graphs to enhance the clustering process. The core concept involves the construction of a graph that assesses the similarity among high-dimensional data points, capable of transformation into a block-diagonal form via an unknown permutation. This is followed by a cluster-ordering procedure that establishes the requisite permutation, thereby facilitating the straightforward identification of clustering structures through the recognition of diagonal blocks in the permuted graph. The principal obstacle addressed in this study is the construction of a graph that inherently possesses a block-diagonal structure, the permutation of this graph to actualize such a structure, and the autonomous identification of diagonal blocks within the permuted graph. To surmount these challenges, we initially devise a block-diagonal constrained self-representation model to create a similarity graph that exhibits a block-diagonal form post-permutation. A gradient descent-based methodology is proposed to resolve this problem effectively. Concurrently, we engineer a traversal algorithm, inspired by DBSCAN, that discerns clusters of high density within the graph and generates an enhanced cluster ordering. The attainment of a block-diagonal structure is then realized through permutation aligned with the traversal sequence, laying a robust foundation for both automated and interactive cluster analysis. Moreover, a novel split-and-refine algorithm is introduced to autonomously identify all diagonal blocks within the pe

关键词： clustering algorithms Noise Indexes clustering methods Shape Partitioning algorithms Databases Cluster analysis DBSCAN block-diagonal property self-representation graph permutation diagonal block identification

来源：评论

学校读者我要写书评

暂无评论

clustering-Aided Grid-Based One-to-One Selection-Driven Evolutionary Algorithm for Multi/Many-Objective Optimization

引用

IEEE ACCESS 2024年 12卷 120612-120623页

作者： Palakonda, Vikas Kang, Jae-Mo Jung, Heechul Kyungpook Natl Univ Dept Artificial Intelligence Daegu 41566 South Korea

Multiobjective evolutionary algorithms are highly effective in solving multiobjective optimization problems (MOPs). The selection strategy, involving mating and environmental selection, is crucial in shaping these algorithms. However, when applied to many-objective optimization (MaOPs) with more than three objectives, existing methods face challenges due to reduced selection pressure and issues in maintaining diversity, making them less efficient. To address these challenges, we present a novel approach in this paper: the clustering-aided grid-based one-to-one selection-driven evolutionary algorithm (ClGrMOEA), designed to handle both MOPs and MaOPs effectively. The proposed ClGrMOEA introduces a hybrid approach that combines clustering-based mating selection with grid-based one-to-one environmental selection to balance convergence and diversity in MOPs and MaOPs. The mating selection employs K-means clustering to partition the objective space and utilizes a convergence indicator based on Euclidean distance to select promising solutions for offspring generation. The environmental selection combines Pareto dominance with grid-based one-to-one selection, using grid coordinate point distance to select promising solutions. An external archive based on Pareto dominance and crowding distance preserves elite individuals. Extensive experiments are conducted on 19 benchmark problems and 16 real-world problems to validate the superior performance of ClGrMOEA compared to seven state-of-the-art algorithms. The experimental results demonstrate that ClGrMOEA significantly outperforms these benchmark algorithms.

关键词： Optimization Statistics Sociology Convergence clustering algorithms Evolutionary computation Benchmark testing Optimization methods Nearest neighbor methods diversity multi-objective optimization many-objective optimization K-means clustering grid settings

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：