检索结果-内蒙古大学图书馆

International Conference on Stability and Control Processes, in Memory of V.I. Zubov (SCP)

作者： Liliya Demidova Yulia Sokolova Ryazan State Radioengineering University Ryazan Russia

The objects classification problem with application of SVM algorithm is considered. The ways of training set formation for the SVM-algorithm, realizing various versions of classification decisions accounting, received with application of fuzzy clustering algorithms, are analyzed. Use possibility of fuzzy clustering algorithms ensemble on the base of the cluster tags vectors similarity matrices for the training set forming is shown.

关键词： clustering algorithms Support vector machines Standards Training Phase change materials Algorithm design and analysis Testing

来源：评论

学校读者我要写书评

暂无评论

Direct Spectral clustering with New Graph Learning for Better Fitting

引用

IEEE Transactions on Knowledge and Data Engineering 2025年第07期37卷 3991-4002页

作者： Kong, Lingyi Xue, Jingjing Nie, Feiping Li, Xuelong Xidian University School of Telecommunications Engineering Xi'an710071 China Xi'an710072 China Northwestern Polytechnical University Key Laboratory of Intelligent Interaction and Applications Ministry of Industry and Information Technology Xi'an710072 China 31 Jinrong Street Beijing100033 China

Traditional spectral clustering methods struggle with scalability and robustness in large datasets due to their reliance on similarity matrices and eigenvalue decomposition. We introduce two innovative models: Rcut-based Coordinate Descent clustering (R-CDC) and Ncut-based Doubly Stochastic clustering (N-DSC). These models integrate graph construction and segmentation into a unified process optimized through the coordinate descent method, significantly enhancing clustering efficacy. A novel graph structure enhances robustness against noise and outliers, simplifying the clustering process and improving outcomes across diverse datasets. Our extensive experiments show that these models surpass existing spectral clustering techniques in managing large-scale data and complex structures. The code can be found in https://***/happyduck-313/R-CDC-and-N-DSC. © 1989-2012 IEEE.

关键词： Optimization Linear Programming clustering algorithms Noise Accuracy Stochastic Processes Robustness Eigenvalues And Eigenfunctions Laplace Equations Heuristic algorithms Coordinate Descent Method clustering Graph Cut Machine Learning Spectral clustering Graph Learning Optimization Process clustering Method Similarity Matrix Graph Structure Graph Construction Coordinate Descent Traditional clustering Coordinate Descent Method Traditional clustering Methods Discretion Objective Function Computational Complexity clustering Algorithm Unsupervised Learning Time Complexity Large Scale Datasets Lagrange Multiplier clustering Results Face Images Adjusted Rand Index Discrete Clusters Weak Connections Affinity Matrix Computational Complexity Reduction Regularization Parameter Graph Based clustering Graph Data

来源：评论

学校读者我要写书评

暂无评论

Modeling and identification of irrigation station using fuzzy c-mean clustering algorithms based on particle swarm optimization

Modeling and identification of irrigation station using fuzz...

引用

International Conference on Systems and Control (ICSC)

作者： Jaouher Chrouta Abderrahmen Zaafouri Mohamed Jemli BSc and MSc degrees in Electrical Engineering in 2010 and 2012 High School of Sciences and Engineering of Tunis (ESSTT) Tunisia ESSTT interested in automated control of nonlinear systems Tunisia High School of Sciences and Techniques of Tunis (ESSTT) Tunisia

The fuzzy clustering model makes up one of the best approaches to show an excellent ability to identify and model nonlinear systems. In literature, several algorithms were used to solve this kind of case. Fuzzy c-means (FCM) is one of the most used clustering methods because it is efficient, straightforward, and easy to implement. However, the FCM has its weakness for example, the algorithm is generally sensitive to initialization and is easily trapped in local optima due to its non-convex objective function. To avoid these problems, heuristic methods introduced by many researchers such as particle swarm optimization (PSO). So, it is a robust strategy for optimization problem. In this paper, a fuzzy clustering method based on FCM and PSO is study which make use of the merits of both algorithms to find a new model of the irrigation station. Experimental results applied to station of irrigation show that the hybrid algorithm (FCM-PSO) is efficient and can reveal encouraging results. Our analysis indicates that the PSO improves the performance of the fuzzy c-means (FCM) algorithm.

关键词： clustering algorithms Irrigation Partitioning algorithms Nonlinear systems Mathematical model Particle swarm optimization Water resources

来源：评论

学校读者我要写书评

暂无评论

A Radar Signal Deinterleaving Method Based on Complex Network and Laplacian Graph clustering

引用

IEEE SIGNAL PROCESSING LETTERS 2024年 31卷 2580-2584页

作者： Guo, Qiang Huang, Shuai Qi, Liangang Li, Daren Kaliuzhnyi, Mykola Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Peoples R China Kharkiv Natl Univ Radio Elect Adv Res Lab UA-61166 Kharkov Ukraine

Radar signal deinterleaving is an essential step in perceiving the battlefield situation and mastering military initiative in the information battlefield. Complex radar systems are rapidly updated and iterated, which exacerbates the possibility of "increasing batch" and "mistaken batch" during radar signal deinterleaving. In this letter, a novel method based on complex networks and Laplacian graph clustering is proposed to improve the accuracy of deinterleaving. First, a complex network is constructed to mine the spatial correlation relationships of the same radar signals. Then, based on the graph characteristics of the Laplacian matrix, the number of cluster centers is solved. Finally, this letter employs Laplacian spectral clustering based on graph segmentation to accomplish radar signal deinterleaving. The results of the experimental simulation demonstrate that the method is capable of effectively tackling the "increasing batch" and "mistaken batch" problems of radar signal deinterleaving, and could reach 99.88% deinterleaving accuracy with high robustness.

关键词： Radar Laplace equations Complex networks clustering algorithms Correlation Signal processing algorithms Eigenvalues and eigenfunctions Complex network increasing batch Laplacian graph clustering mistaken batch radar signal deinterleaving

来源：评论

学校读者我要写书评

暂无评论

Reducing Communication and Merging Overheads for Distributed clustering algorithms on the Cloud

Reducing Communication and Merging Overheads for Distributed...

引用

International Conference on Cloud Computing and Big Data (CCBD)

作者： Chun-Chieh Chen Tze-Yu Chen Jen-Wei Huang Ming-Syan Chen Graduate Institute of Networking and Multimedia National Taiwan University Taipei Taiwan Department of Electrical Engineering National Cheng Kung University Tainan Taiwan

Many distributed clustering algorithms have been proposed to speed up data clustering on huge database. However, the existing distributed clustering algorithms still suffer from many issues on distributed system such as data synchronization, insufficient scalability, and maintenance difficulties. In this paper, we propose two distributed clustering algorithms named DDC and DGC, which are based on the cloud computing technique. The main ideas of proposed algorithms are to achieve load balance according to an efficient data partition, to cluster more data on many machines in parallel without data dependency, and to merge the result on a machine efficiently with minimal information overlap. The experimental results show that DDC and DGC are able to reduce the execution time and achieve great scalability on the cloud.

关键词： clustering algorithms Partitioning algorithms Algorithm design and analysis Cloud computing Distributed databases Scalability Merging

来源：评论

学校读者我要写书评

暂无评论

Scale-Driven Tensor Representation-Based Multiview clustering

引用

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025年

作者： Zhang, Chuanbin Chen, Long Ding, Weiping Zhao, Kai Shi, Zhaoyin Wang, Yingxu Chen, C. L. Philip Zhaoqing Univ Sch Comp Sci & Software Zhaoqing 526061 Peoples R China Univ Macau Fac Sci & Technol Dept Comp & Informat Sci Taipa Macau Peoples R China Nantong Univ Sch Artificial Intelligence & Comp Sci Nantong 226019 Peoples R China City Univ Macau Fac Data Sci Taipa Macau Peoples R China Chongqing Univ Sch Automat Chongqing 400044 Peoples R China Shenzhen Univ Coll Mechatron & Control Engn Shenzhen 518060 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510641 Peoples R China

Real-world data tends to exhibit an inherent hierarchical structure, providing a natural multiview perspective where features at different scales can be treated as distinct views. However, most existing multiview clustering algorithms primarily focus on the inter-sample relationships at a single level. These methods overlook the hierarchical structures present in the data and are specifically designed for native multiview data. This article introduces a comprehensive multiview clustering framework that transforms both typical data and images into a unified multiview feature representation. The framework allows for extracting multiscale features from the raw data and clustering different types of data with the same algorithm. A novel scale-driven pre-processing approach unifies the feature structure across various data types and explores local relationships among samples at multiple scales. Features at larger scales delineate the global cluster contours, while features at smaller scales reveal fine-grained local details. Subsequently, the proposed method learns the view-specific partitions from different scales of views and derives consensus features through tensor low-rank representation. By optimizing these consensus features, the approach effectively captures the precise cluster shapes from coarse to fine-grained levels. The final label indicator matrix is directly obtained from these consensus features. To demonstrate the effectiveness and versatility of the proposed method, we conducted experimental comparisons with state-of-the-art (SOTA) algorithms in both multiview clustering and image segmentation across diverse datasets. The source code and datasets are released at https://***/ChuanbinZhang/SDTR

关键词： Tensors clustering algorithms Feature extraction Image segmentation Data mining Transforms Technological innovation Matrix decomposition Computer science Urban areas Low-rank tensor multiview clustering scale-driven unsupervised learning

来源：评论

学校读者我要写书评

暂无评论

A Review of Uncertain Data Stream clustering algorithms

A Review of Uncertain Data Stream Clustering Algorithms

引用

International Conference on Internet Computing in Science and Engineering (ICICSE)

作者： Yue Yang Zhuo Liu Zhidan Xing College of Computer Science and Technology Harbin Engineering University Harbin China College of Automation

Because of data uncertainty and time and space limitations in clustering process, clustering uncertain data stream becomes a very challenging task. A lot of processing algorithms has been proposed continuously. This paper reviews clustering algorithms of uncertain data stream and dissects the advantages and disadvantages of these algorithms for future development.

关键词： clustering algorithms Uncertainty Algorithm design and analysis Data models Data structures Heuristic algorithms Classification algorithms

来源：评论

学校读者我要写书评

暂无评论

Association Rules for clustering algorithms for Data Mining of Temporal Power Ramp Balance

Association Rules for Clustering Algorithms for Data Mining ...

引用

International Conference on Cyberworlds

作者： Nurseda Yildirim Bahri Uzunoglu Department of Engineering Sciences The Ångström Laboratory Uppsala Sweden

Power ramp estimation is utmost importance for wind power plants which will be the focus of this paper. Power ramps are caused by intermittent supply of wind power generation. This is an important problem in the power system that needs to keep the load and generation at balance at all times while any unbalance leads to price volatility, grid security issues that can create power stability problems that leads to financial losses. In this study, K-means clustering and association rules of apriori algorithm are implemented to analyze and predict wind power ramp occurrences based on 10 minutes temporal SCADA data of power from records of Ayyildiz wind farm. Power ramps are computed from this data. Five wind turbines with no dissimilarity measure in space were clustered based on temporal data. The power ramp data are analyzed by the K-means algorithm for calculation of their cluster means and cluster labels. Association rules of data mining algorithm were employed to analyze temporal ramp occurrences between wind turbines. Each turbine impact on the other turbines were analyzed as different transactions at each time step. Operational rules based on these transactions are discovered by an apriori association rule algorithm for operation room decision making. Discovery of association rules from an apriori algorithm can help with decision making for power system operator.

关键词： Association rules clustering algorithms Yttrium Turbines Wind farms Production

来源：评论

学校读者我要写书评

暂无评论

Analysis of standard clustering algorithms for grouping MEDLINE abstracts into evidence-based medicine intervention categories

Analysis of standard clustering algorithms for grouping MEDL...

引用

International Conference on Stability and Control Processes, in Memory of V.I. Zubov (SCP)

作者： Vladimir Dobrynin Yulia Balykina Michael Kamalov St. Petersburg State University St. Petersburg Russia

The paper describes a process of clustering of article abstracts, taken from the largest bibliographic life sciences and biomedical information MEDLINE database into categories that correspond to types of medical interventions - types of patient treatments. Experiments were carried out to evaluate the quality of clustering for the following algorithms: K-means; K-means++; Hierarchical clustering, SIB (Sequential information bottleneck) together with the LSA (Latent Semantic Analysis) methods and MI (Mutual Information) which allow selecting feature vectors. Best results of clustering were achieved by K-means++ together with LSA then 210-dimensional space was chosen: Purity = 0.5719, Entropy = 1.3841, Normalized Entropy = 0.6299.

关键词： clustering algorithms Entropy Mutual information Algorithm design and analysis Libraries Semantics Information retrieval

来源：评论

学校读者我要写书评

暂无评论

Interference-Aware clustering algorithms in Multi-relay Cellular Networks

Interference-Aware Clustering Algorithms in Multi-relay Cell...

引用

International Conference on Computational Science and its Applications (ICCSA)

作者： Hae Sol Lee Il-Min Kim Dong In Kim Seokki Kim Kwangae Lim College of Information and Communication Engineering Sungkyunkwan University (SKKU) Korea Electrical and Computer Engineering Queen's University Canada Electronics and Telecommunications Research Institute (ETRI) Korea

ISBN: (纸本)9781467373685

We consider the downlink of a wireless cellular network which consists of one Macro Base Station (MBS), multiple relays and User Equipments (UEs). In this scenario, we propose relay clustering algorithms to address the problem of inter-cluster interference. In each cluster, we adopt a time-division relaying scheme to avoid intra-cluster interference, but the inter-cluster interference still remains. In interference-aware network, an optimal clustering algorithm requires an exhaustive search, which is infeasible. Hence, we propose a clustering algorithm which improves the system capacity by exploiting the inter-cluster information. But this algorithm is implemented in a centralized manner with high complexity that requires the calculation of inter-cluster information. To circumvent this, we propose a cluster-wise distributed algorithm with manageable complexity, which shows the performance within acceptable range from the optimal one, far better than simple and intuitive algorithms because of the effect of inter-cluster interference.

关键词： Relays clustering algorithms Interference Bismuth Distributed algorithms Wireless communication Complexity theory

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：