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Hybrid knowledge reasoning over knowledge hypergraph:Inductive,deductive,and abductive 认领 引用
1
作者 Ling Tian Lei Gao +3 位作者 Ben Zhang Xiao Liu Yi-Nong Shi Hui Gao 《Journal of Electronic Science and Technology》 EI CAS CSCD 2026年第2期11-26,共16页
Traditional knowledge reasoning methods,which are predominantly reliant on static rules and structured data,often struggle to adapt to the ambiguity and dynamic evolution of real-world scenarios.To overcome these limi... Traditional knowledge reasoning methods,which are predominantly reliant on static rules and structured data,often struggle to adapt to the ambiguity and dynamic evolution of real-world scenarios.To overcome these limitations,this study proposes a novel reasoning framework based on a three-layered knowledge hypergraph.Core innovation lies in the synergy of inductive,deductive,and abductive reasoning mechanisms to enhance both reliability and interpretability.Specifically,hypergraph-based inductive reasoning extracts robust evolutionary patterns by mining the historical subgraph structures.Deductive reasoning ensures transparency by constructing tree-shaped inference paths,whereas abductive reasoning establishes causal traceability by forming evidence chains from historical contexts.Experimental evaluations on the Integrated Crisis Early Warning System(ICEWS)dataset demonstrate that the proposed approach significantly outperforms existing methods in terms of accuracy and interpretability,thereby offering a scalable solution for complex event analysis. 展开更多
关键词 Abductive reasoning Deductive reasoning Inductive reasoning Knowledge hypergraphs Knowledge reasoning
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Message passing method for social contagion in hypergraphs 认领 引用
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作者 Hui Leng Zhao-Yan Wu Rong Wang 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第5期538-547,共10页
The emergence of hypergraphs has solved the problem that the interactions between nodes are insufficient to describe the complex relationships among multiple individuals.In this paper,we model social contagion with th... The emergence of hypergraphs has solved the problem that the interactions between nodes are insufficient to describe the complex relationships among multiple individuals.In this paper,we model social contagion with the reinforcement effect on hypergraphs,where hyperedges disseminate information to nodes,and nodes upload information to hyperedges.In order to reduce the complexity of high-order interactions on the propagation,hypergraphs are mapped to factor graphs,where hyperedges are encoded to factor nodes,and the connection between a node and a factor node indicates that the node is located in the hyperedge.Taking into account the heterogeneity of nodes and hyperedges,we establish the message passing evolution equations about each node based on the factor graph.Finally,we carry out numerical simulations by iterating the message passing equations.We find that the probability of the adopted state decreases before the outbreak of social contagion,and the final adopting scale suddenly increases as the transmission rates increase,which are caused by the combined action of high-order interactions and the social reinforcement effect.Significantly,the final adopting scale presents a step-like variation when the adopting threshold of hyperedges changes. 展开更多
关键词 social contagion hypergraph message passing method
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A hypergraph neural network-based framework for identification of Parkinson's disease subtypes 认领 引用
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作者 Meili Lu Hongbao Lu 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第7期864-874,共11页
Parkinson's disease(PD)exhibits significant phenotypic heterogeneity,which complicates clinical management and underscores the need for precise subtyping.Existing subtyping approaches often rely on a single modali... Parkinson's disease(PD)exhibits significant phenotypic heterogeneity,which complicates clinical management and underscores the need for precise subtyping.Existing subtyping approaches often rely on a single modality,such as clinical assessments,failing to capture the complex,multi-faceted nature of the disease.This paper proposes a novel computational framework that integrates multi-modal data,specifically preprocessed functional MRI,DNA methylation,and clinical behavioral assessments,for PD subtyping.The methodology involves constructing individual hypergraphs for each modality using K-nearest neighbors(KNN),followed by the integration of these hypergraphs into a unified,multi-modal hypergraph using similarity network fusion(SNF).This consolidated hypergraph is then processed via a hypergraph neural network(HGNN)utilizing hyperedge convolution to cluster patients into distinct subtypes.Our experimental results demonstrate that this approach effectively identifies PD subtypes with significant clinical and biological relevance.We provide a comprehensive analysis of the model's performance and further validate the reliability of the identified subtypes through post-hoc statistical tests.This study highlights the potential of graph-based machine learning in disentangling disease heterogeneity,paving the way for personalized therapeutic strategies and improved patient outcomes. 展开更多
关键词 hypergraph neural network similarity network fusion Parkinson’s disease subtypes multi-modal data
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Evolutionary hypergraph dismantling via deep reinforcement learning 认领 引用
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作者 Junjie Qian Wenlan Wang +3 位作者 Hanyun Wang Qiqi Wang Yao Zhang Huijia Li 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第6期886-898,共13页
Assessing the vulnerability of complex systems requires effective hypergraph dismantling strategies,yet existing methods struggle with the dynamic nature of cascading failures and the rugged optimization landscapes of... Assessing the vulnerability of complex systems requires effective hypergraph dismantling strategies,yet existing methods struggle with the dynamic nature of cascading failures and the rugged optimization landscapes of high-order networks.In this paper,we propose a novel framework:hypergraph dismantling via evolutionary deep reinforcement learning(HD-EDR).First,we model a realistic dismantling environment incorporating hyperdegree-based and residual-capacitybased load redistribution mechanisms.Second,we introduce a hybrid learning architecture that synergizes the global exploration of evolutionary strategies with the gradient-based exploitation of deep reinforcement learning.A bidirectional parameter synchronization mechanism is designed to prevent the agent from being trapped in local optima.Furthermore,we integrate an inductive encoder to capture the evolving high-order dependencies of the residual network in real time.Extensive experiments across nine real-world datasets demonstrate that our framework significantly outperforms state-of-the-art baselines,providing a highly effective and robust strategy for maximizing structural damage in high-order networks. 展开更多
关键词 network dismantling hypergraph deep reinforcement learning evolutionary strategies combinatorial optimization
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Hypergraphs for Covering Trees and Approximating Steiner Trees 认领 引用
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作者 Miklós Molnár 《Computers, Materials & Continua》 SCIE EI 2026年第7期2171-2185,共15页
This article presents a particular tree covering technique.To cover a set of nodes belonging to an unknown tree,a set of connected small Steiner trees is proposed.These Steiner trees can be represented as hyperedges,a... This article presents a particular tree covering technique.To cover a set of nodes belonging to an unknown tree,a set of connected small Steiner trees is proposed.These Steiner trees can be represented as hyperedges,and a chain or tree of hyperedges provides the cover.The model allows the calculation of approximate(partial)spanning trees in graphs.The idea of covering a set of nodes by hyperedges can be used directly in Steiner heuristics.The NP-hard Steiner problem in graphs is one of the most studied graph-related problems.Several heuristics are known to give approximated solutions.The classical approximations of the Steiner problem apply shortest paths.We present a generalized metric closure that can be constructed from hyperedges of limited size,and new approximations based on the generalized metric closure are proposed.A connected hyperedge set(without loops)approximates a Steiner tree.The paper also presents a performance analysis of the proposed heuristics.The variation in hyperedge size is analyzed.An interesting result is that using larger hyperedges significantly improves the algorithm’s efficiency. 展开更多
关键词 Graphs spanning problems hypergraphs steiner problem metric closure approximation
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Three-party semi-quantum dialogue enhanced with Grover's algorithm based encoding and hypergraph access control 认领 引用
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作者 Rui Tao Jin-Zhe Jiang Zhi-Hua Zhang 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第7期435-447,共13页
We introduce a new three-party semi-quantum dialogue(3P-SQD)protocol that combines GHZ-state-based semiquantum communication,a Grover's algorithm-driven 2-bit encoding scheme,and hypergraph-based access control.In... We introduce a new three-party semi-quantum dialogue(3P-SQD)protocol that combines GHZ-state-based semiquantum communication,a Grover's algorithm-driven 2-bit encoding scheme,and hypergraph-based access control.In each round,the fully quantum participant Alice sends two bits,whereas the semi-quantum participants Bob and Charlie,restricted to semi-quantum operations such as measurements in the computational basis and reflection,each transmit one bit.The protocol incorporates probe state checking,Grover's algorithm-based encoding,and hypergraph-based authorization.It achieves information-theoretic security and controlled access,while preserving high message throughput and imposing no additional requirements on the semi-quantum users. 展开更多
关键词 semi-quantum dialogue Grover’s algorithm based encoding hypergraph access structure quantum communication
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3D Hand Pose Estimation Using Semantic Dynamic Hypergraph Convolutional Networks 认领 引用
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作者 WU Yalei LI Jinghua +2 位作者 KONG Dehui LI Qianxing YIN Baocai 《Journal of Shanghai Jiaotong university(Science)》 EI 2025年第5期855-865,共11页
Due to self-occlusion and high degree of freedom,estimating 3D hand pose from a single RGB image is a great challenging problem.Graph convolutional networks(GCNs)use graphs to describe the physical connection relation... Due to self-occlusion and high degree of freedom,estimating 3D hand pose from a single RGB image is a great challenging problem.Graph convolutional networks(GCNs)use graphs to describe the physical connection relationships between hand joints and improve the accuracy of 3D hand pose regression.However,GCNs cannot effectively describe the relationships between non-adjacent hand joints.Recently,hypergraph convolutional networks(HGCNs)have received much attention as they can describe multi-dimensional relationships between nodes through hyperedges;therefore,this paper proposes a framework for 3D hand pose estimation based on HGCN,which can better extract correlated relationships between adjacent and non-adjacent hand joints.To overcome the shortcomings of predefined hypergraph structures,a kind of dynamic hypergraph convolutional network is proposed,in which hyperedges are constructed dynamically based on hand joint feature similarity.To better explore the local semantic relationships between nodes,a kind of semantic dynamic hypergraph convolution is proposed.The proposed method is evaluated on publicly available benchmark datasets.Qualitative and quantitative experimental results both show that the proposed HGCN and improved methods for 3D hand pose estimation are better than GCN,and achieve state-of-the-art performance compared with existing methods. 展开更多
关键词 hand pose estimation hypergraph convolution dynamic hypergraph convolution semantic dynamic hypergraph convolution
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Multi-Scale Dynamic Hypergraph Convolutional Network for Traffic Flow Forecasting 认领 引用
8
作者 DONG Zhaoxian YU Shuo SHEN Yanming 《Journal of Shanghai Jiaotong university(Science)》 EI 2025年第5期880-888,共9页
This paper focuses on the problem of traffic flow forecasting,with the aim of forecasting future traffic conditions based on historical traffic data.This problem is typically tackled by utilizing spatio-temporal graph... This paper focuses on the problem of traffic flow forecasting,with the aim of forecasting future traffic conditions based on historical traffic data.This problem is typically tackled by utilizing spatio-temporal graph neural networks to model the intricate spatio-temporal correlations among traffic data.Although these methods have achieved performance improvements,they often suffer from the following limitations:These methods face challenges in modeling high-order correlations between nodes.These methods overlook the interactions between nodes at different scales.To tackle these issues,in this paper,we propose a novel model named multi-scale dynamic hypergraph convolutional network(MSDHGCN)for traffic flow forecasting.Our MSDHGCN can effectively model the dynamic higher-order relationships between nodes at multiple time scales,thereby enhancing the capability for traffic forecasting.Experiments on two real-world datasets demonstrate the effectiveness of the proposed method. 展开更多
关键词 traffic flow forecasting dynamic hypergraph hypergraph structure learning multi-time scale
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On the Coprime Labelings of Hypergraph 认领 引用
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作者 ZHANG Zizhou ZHANG Shaohua 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2025年第1期57-59,共3页
Graph labeling is the assignment of integers to the vertices,edges,or both,subject to certain conditions.Accordingly,hypergraph labeling is also the assignment of integers to the vertices,edges,or both,subject to cert... Graph labeling is the assignment of integers to the vertices,edges,or both,subject to certain conditions.Accordingly,hypergraph labeling is also the assignment of integers to the vertices,edges,or both,subject to certain conditions.This paper is to generalize the coprime labelings of graph to hypergraph.We give the definition of coprime labelings of hypergraph.By using Rosser-Schoenfeld's inequality and the coprime mapping theorem of Pomerance and Selfridge,we prove that some linear hypergraphs are prime. 展开更多
关键词 coprime mapping theorem of Pomerance and Selfridge linear hypergraphs prime hypergraphs
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Hypergraph Computation 认领 引用 被引量：1
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作者 Yue Gao Shuyi Ji +1 位作者 Xiangmin Han Qionghai Dai 《Engineering》 SCIE EI CAS CSCD 2024年第9期188-201,共14页
Practical real-world scenarios such as the Internet,social networks,and biological networks present the challenges of data scarcity and complex correlations,which limit the applications of artificial intelligence.The ... Practical real-world scenarios such as the Internet,social networks,and biological networks present the challenges of data scarcity and complex correlations,which limit the applications of artificial intelligence.The graph structure is a typical tool used to formulate such correlations,it is incapable of modeling highorder correlations among different objects in systems;thus,the graph structure cannot fully convey the intricate correlations among objects.Confronted with the aforementioned two challenges,hypergraph computation models high-order correlations among data,knowledge,and rules through hyperedges and leverages these high-order correlations to enhance the data.Additionally,hypergraph computation achieves collaborative computation using data and high-order correlations,thereby offering greater modeling flexibility.In particular,we introduce three types of hypergraph computation methods:①hypergraph structure modeling,②hypergraph semantic computing,and③efficient hypergraph computing.We then specify how to adopt hypergraph computation in practice by focusing on specific tasks such as three-dimensional(3D)object recognition,revealing that hypergraph computation can reduce the data requirement by 80%while achieving comparable performance or improve the performance by 52%given the same data,compared with a traditional data-based method.A comprehensive overview of the applications of hypergraph computation in diverse domains,such as intelligent medicine and computer vision,is also provided.Finally,we introduce an open-source deep learning library,DeepHypergraph(DHG),which can serve as a tool for the practical usage of hypergraph computation. 展开更多
关键词 High-order correlation Hypergraph structure modeling Hypergraph semantic computing Efficient hypergraph computing Hypergraph computation framework
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Identification of vital nodes based on global and local features in hypergraphs 认领 引用
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作者 Li Liang Li-Yao Qi Shi-Cai Gong 《Chinese Physics B》 SCIE EI CAS CSCD 2025年第10期169-178,共10页
Hypergraphs,which encapsulate interactions of higher-order beyond mere pairwise connections,are essential for representing polyadic relationships within complex systems.Consequently,an increasing number of researchers... Hypergraphs,which encapsulate interactions of higher-order beyond mere pairwise connections,are essential for representing polyadic relationships within complex systems.Consequently,an increasing number of researchers are focusing on the centrality problem in hypergraphs.Specifically,researchers are tackling the challenge of utilizing higher-order structures to effectively define centrality metrics.This paper presents a novel approach,LGK,derived from the K-shell decomposition method,which incorporates both global and local perspectives.Empirical evaluations indicate that the LGK method provides several advantages,including reduced time complexity and improved accuracy in identifying critical nodes in hypergraphs. 展开更多
关键词 hypergraph vital nodes K-shell decomposition susceptible-infected-recovered(SIR)model
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Optimal synchronization of higher-order Kuramoto model on hypergraphs 认领 引用
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作者 Chong-Yang Wang Bi-Yun Ji Linyuan Lu 《Chinese Physics B》 SCIE EI CAS CSCD 2025年第7期231-238,共8页
Complex networks play a crucial role in the study of collective behavior,encompassing the analysis of dynamical properties and network topology.In real-world systems,higher-order interactions among multiple entities a... Complex networks play a crucial role in the study of collective behavior,encompassing the analysis of dynamical properties and network topology.In real-world systems,higher-order interactions among multiple entities are widespread and significantly influence collective dynamics.Here,we extend the synchronization alignment function framework to hypergraphs of arbitrary order by leveraging the multi-order Laplacian matrix to encode higher-order interactions.Our findings reveal that the upper bound of synchronous behavior is determined by the maximum eigenvalue of the multi-order Laplacian matrix.Furthermore,we decompose the contribution of each hyperedge to this eigenvalue and utilize it as a basis for designing an eigenvalue-based topology modification algorithm.This algorithm effectively enhances the upper bound of synchronous behavior without altering the total number of higher-order interactions.Our study provides new insights into dynamical optimization and topology tuning in hypergraphs,advancing the understanding of the interplay between higher-order interactions and collective dynamics. 展开更多
关键词 synchronization optimization hypergraph complex network
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A local-global dynamic hypergraph convolution with multi-head flow attention for traffic flow forecasting 认领 引用
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作者 ZHANG Hong LI Yang +3 位作者 LUO Shengjun ZHANG Pengcheng ZHANG Xijun YI Min 《High Technology Letters》 EI CAS 2025年第3期246-256,共11页
Traffic flow prediction is a crucial element of intelligent transportation systems.However,accu-rate traffic flow prediction is quite challenging because of its highly nonlinear,complex,and dynam-ic characteristics.To... Traffic flow prediction is a crucial element of intelligent transportation systems.However,accu-rate traffic flow prediction is quite challenging because of its highly nonlinear,complex,and dynam-ic characteristics.To address the difficulties in simultaneously capturing local and global dynamic spatiotemporal correlations in traffic flow,as well as the high time complexity of existing models,a multi-head flow attention-based local-global dynamic hypergraph convolution(MFA-LGDHC)pre-diction model is proposed.which consists of multi-head flow attention(MHFA)mechanism,graph convolution network(GCN),and local-global dynamic hypergraph convolution(LGHC).MHFA is utilized to extract the time dependency of traffic flow and reduce the time complexity of the model.GCN is employed to catch the spatial dependency of traffic flow.LGHC utilizes down-sampling con-volution and isometric convolution to capture the local and global spatial dependencies of traffic flow.And dynamic hypergraph convolution is used to model the dynamic higher-order relationships of the traffic road network.Experimental results indicate that the MFA-LGDHC model outperforms current popular baseline models and exhibits good prediction performance. 展开更多
关键词 traffic flow prediction multi-head flow attention graph convolution hypergraph learning dynamic spatio-temporal properties
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Hypergraph-Based Asynchronous Event Processing for Moving Object Classification 认领 引用
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作者 YU Nannan WANG Chaoyi +4 位作者 QIAO Yu WANG Yuxin ZHENG Chenglin ZHANG Qiang YANG Xin 《Journal of Shanghai Jiaotong university(Science)》 EI 2025年第5期952-961,共10页
Unlike traditional video cameras,event cameras capture asynchronous event streams in which each event encodes pixel location,triggers’timestamps,and the polarity of brightness changes.In this paper,we introduce a nov... Unlike traditional video cameras,event cameras capture asynchronous event streams in which each event encodes pixel location,triggers’timestamps,and the polarity of brightness changes.In this paper,we introduce a novel hypergraph-based framework for moving object classification.Specifically,we capture moving objects with an event camera,to perceive and collect asynchronous event streams in a high temporal resolution.Unlike stacked event frames,we encode asynchronous event data into a hypergraph,fully mining the high-order correlation of event data,and designing a mixed convolutional hypergraph neural network for training to achieve a more efficient and accurate motion target recognition.The experimental results show that our method has a good performance in moving object classification(e.g.,gait identification). 展开更多
关键词 hypergraph learning event stream moving object classification
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Identifying important nodes of hypergraph:An improved PageRank algorithm 认领 引用
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作者 Yu-Hao Piao Jun-Yi Wang Ke-Zan Li 《Chinese Physics B》 SCIE EI CAS CSCD 2025年第4期162-171,共10页
Hypergraphs can accurately capture complex higher-order relationships,but it is challenging to identify their important nodes.In this paper,an improved PageRank(ImPageRank)algorithm is designed to identify important n... Hypergraphs can accurately capture complex higher-order relationships,but it is challenging to identify their important nodes.In this paper,an improved PageRank(ImPageRank)algorithm is designed to identify important nodes in a directed hypergraph.The algorithm introduces the Jaccard similarity of directed hypergraphs.By comparing the numbers of common neighbors between nodes with the total number of their neighbors,the Jaccard similarity measure takes into account the similarity between nodes that are not directly connected,and can reflect the potential correlation between nodes.An improved susceptible–infected(SI)model in directed hypergraph is proposed,which considers nonlinear propagation mode and more realistic propagation mechanism.In addition,some important node evaluation methods are transferred from undirected hypergraphs and applied to directed hypergraphs.Finally,the ImPageRank algorithm is used to evaluate the performance of the SI model,network robustness and monotonicity.Simulations of real networks demonstrate the excellent performance of the proposed algorithm and provide a powerful framework for identifying important nodes in directed hypergraphs. 展开更多
关键词 hypergraph important node PageRank susceptible-infected(SI)model centrality index
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THE SPECTRAL RADIUS OF UNIFORM HYPERGRAPH DETERMINED BY THE SIGNLESS LAPLACIAN MATRIX 认领 引用
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作者 HE Fang-guo 《数学杂志》 2025年第1期1-12,共12页
This paper studies the problem of the spectral radius of the uniform hypergraph determined by the signless Laplacian matrix.The upper bound of the spectral radius of a uniform hypergraph is obtained by using Rayleigh ... This paper studies the problem of the spectral radius of the uniform hypergraph determined by the signless Laplacian matrix.The upper bound of the spectral radius of a uniform hypergraph is obtained by using Rayleigh principle and the perturbation of the spectral radius under moving the edge operation,and the extremal hypergraphs are characterized for both supertree and unicyclic hypergraphs.The spectral radius of the graph is generalized. 展开更多
关键词 spectral radius uniform hypergraph Signless Laplasian matrix
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Data-driven Product Functional Confguration:Patent Data and Hypergraph 认领 引用 被引量：4
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作者 Wenguang Lin Xiaodong Liu Renbin Xiao 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2022年第4期108-124,共17页
The product functional confguration(PFC)is typically used by frms to satisfy the individual requirements of customers and is realized based on market analysis.This study aims to help frms analyze functions and realize... The product functional confguration(PFC)is typically used by frms to satisfy the individual requirements of customers and is realized based on market analysis.This study aims to help frms analyze functions and realize functional confgurations using patent data.This study frst proposes a patent-data-driven PFC method based on a hypergraph network.It then constructs a weighted network model to optimize the combination of product function quantity and object from the perspective of big data,as follows:(1)The functional knowledge contained in the patent is extracted.(2)The functional hypergraph is constructed based on the co-occurrence relationship between patents and applicants.(3)The function and patent weight are calculated from the patent applicant’s perspective and patent value.(4)A weight calculation model of the PFC is developed.(5)The weighted frequent subgraph algorithm is used to obtain the optimal function combination list.This method is applied to an innovative design process of a bathroom shower.The results indicate that this method can help frms detach optimal function candidates and develop a multifunctional product. 展开更多
关键词 Hypergraph Functional confguration Patent analysis Weighted frequent subgraph algorithm
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A Key Management Scheme Based on Hypergraph for Fog Computing 认领 引用 被引量：2
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作者 Zhi Li Yanzhu Liu +3 位作者 Di Liu Chunyang Li Wei Cui Guanglin Hu 《China Communications》 SCIE CSCD 2018年第11期158-170,共13页
Fog computing is a new paradigm supporting the stringent requirements of mobility applications by bridging cloud computing and smart devices. Since the smart devices may be deployed in dynamic areas where are out of s... Fog computing is a new paradigm supporting the stringent requirements of mobility applications by bridging cloud computing and smart devices. Since the smart devices may be deployed in dynamic areas where are out of strict monitoring and protection, fog computing requires security protections to ensure confidentiality and integrity. In this article, to deal with security requirements and considering the distinctive features, a key management based on hypergraph schemed is designed. Firstly, based on the key hypergraph, the three hierarchy architecture of fog computing is divided into two subnetworks. Furthermore, each key management process of both two subnetworks is designed to satisfy the operational and security requirements of fog computing. Finally, the performance evaluation and numerical simulation have been provided to validate the proposed scheme. 展开更多
关键词 fog computing fog security keymanagement hypergraph
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Hypergraph High-Dimension Clustering Algorithm for Optimized Cooperative Wireless Multicast 认领 引用 被引量：1
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作者 Chen Yueyun Liu Wei +1 位作者 Lin Fuhong Zhou Xianwei 《China Communications》 SCIE CSCD 2012年第8期135-139,共5页
In order to guarantee the wireless multicast throughput at a minimum cost,we propose a layered hypergraph high-dimension clustering algorithm(LayerHC)considering the channels and statistical locations of mobile member... In order to guarantee the wireless multicast throughput at a minimum cost,we propose a layered hypergraph high-dimension clustering algorithm(LayerHC)considering the channels and statistical locations of mobile members.The algorithm can achieve a minimum multicast spanning tree to obtain a minimum number of relays and effective cooperative areas with low computational complexity. 展开更多
关键词 cooperative wireless multicast hypergraph high-dimension clustering
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New Clustering Method in High-Di mensional Space Based on Hypergraph-Models 认领 引用 被引量：1
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作者 陈建斌 王淑静 宋瀚涛 《Journal of Beijing Institute of Technology》 EI CAS 2006年第2期156-161,共6页
To overcome the limitation of the traditional clustering algorithms which fail to produce meaningful clusters in high-dimensional, sparseness and binary value data sets, a new method based on hypergraph model is propo... To overcome the limitation of the traditional clustering algorithms which fail to produce meaningful clusters in high-dimensional, sparseness and binary value data sets, a new method based on hypergraph model is proposed. The hypergraph model maps the relationship present in the original data in high dimensional space into a hypergraph. A hyperedge represents the similarity of attrlbute-value distribution between two points. A hypergraph partitioning algorithm is used to find a partitioning of the vertices such that the corresponding data items in each partition are highly related and the weight of the hyperedges cut by the partitioning is minimized. The quality of the clustering result can be evaluated by applying the intra-cluster singularity value. Analysis and experimental results have demonstrated that this approach is applicable and effective in wide ranging scheme. 展开更多
关键词 high-dimensional clustering hypergraph model data mining
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