检索结果-南通市图书馆

fuzzy clustering with the Structural α-Entropy

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Chinese Journal of Electronics 2018年第6期27卷 1118-1125页

作者： LI Kai GAO Yan School of Cyber Security and Computer Hebei University

We study fuzzy clustering with the structural α-entropy and present a unified framework for fuzzy clustering with fuzzy entropy, which can be regarded fuzzy clustering with fuzzy entropy as its special case. Then,aiming at weighting exponent m equal to the structuralα-entropy index α in the presented unified framework, we obtain the fuzzy membership degrees and cluster centers using Lagrange method. Further, we propose the Structural α-entropy based fuzzy c-means(SEFCM) ***, to solve clustering of the complicated data, we also present the Structural α-entropy based kernel fuzzy c-means(SEKFCM) algorithm. In experiment, some University of California Irvine(UCI) data sets and synthetic data sets are used to test the performance of the presented algorithms and the role of the structural α-entropy. The experimental results show that the presented algorithms obtain better clustering result.

关键词： Structural α-entropy Unified framework Kernel function fuzzy clustering Structural α-entropy based fuzzy c-means(SEFCM) algorithm Structural α-entropy based kernel fuzzy c-means(SEKFCM) algorithm

fuzzy clustering with Novel Separable Criterion

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Tsinghua Science and Technology 2006年第1期11卷 50-53页

作者：尹中航唐元钢孙富春孙增圻 Department of Computer Science and Technology Tsinghua UniversityBeijing 100084 China

fuzzy clustering has been used widely in pattern recognition, image processing, and data analysis An improved fuzzy clustering algorithm was developed based on the conventional fuzzy c-means （FCM） to obtain better quality clustering results. The update equations for the membership and the cluster center are derived from the alternating optimization algorithm. Two fuzzy scattering matrices in the objective function assure the compactness between data points and cluster centers, and also strengthen the separation between cluster centers in terms of a novel separable criterion. The clustering algorithm properties are shown to be an improvement over the FCM method＇s properties. Numerical simulations show that the clustering algorithm gives more accurate clustering results than the FCM method.

关键词： fuzzy c-means (FCM) alternating optimization fuzzy clustering

fuzzy clustering Method for Web User Based on Pages Classification

在线全文

学校读者我要写书评

暂无评论

Wuhan University Journal of Natural Sciences 2004年第5期9卷 553-556页

作者： ZHANLi-qiang LIUDa-xin InstituteofEconomicandManagement NortheastForestryUniversityHarbin150010Heilong.jiangChina DepartmentofScienceofComputer.HarbinEngineeringUniversity Harbin150001HcilongjiangChina

A new method for Web users fuzzy clustering based on analysis of user interest characteristic is proposed in this article. The method first defines page fuzzy categories according to the links on the index page of the site, then computes fuzzy degree of cross page through aggregating on data of Web log. After that, by using fuzzy comprehensive evaluation method, the method constructs user interest vectors according to page viewing times and frequency of hits, and derives the fuzzy similarity matrix from the interest vectors for the Web users. Finally, it gets the clustering result through the fuzzy clustering method. The experimental results show the effectiveness of the method. Key words Web log mining - fuzzy similarity matrix - fuzzy comprehensive evaluation - fuzzy clustering CLC number TP18 - TP311 - TP391 Foundation item: Supported by the Natural Science Foundation of Heilongjiang Province of China (F0304)Biography: ZHAN Li-qiang (1966-), male, Lecturer, Ph. D. research direction: the theory methods of data mining and theory of database.

关键词： Web log mining fuzzy similarity matrix fuzzy comprehensive evaluation fuzzy clustering

Metamodel-based Global Optimization Using fuzzy clustering for Design Space Reduction

在线全文

学校读者我要写书评

暂无评论

Chinese Journal of Mechanical Engineering 2013年第5期26卷 928-939页

作者： LI Yulin LIU Li LONG Teng DONG Weili Key Laboratory of Dynamics and Control of Flight Vehicle of Ministry of Education School of Aerospace EngineeringBeijing Institute of Technology

High fidelity analysis are utilized in modern engineering design optimization problems which involve expensive black-box *** computation-intensive engineering design problems,efficient global optimization methods must be developed to relieve the computational burden.A new metamodel-based global optimization method using fuzzy clustering for design space reduction(MGO-FCR) is *** uniformly distributed initial sample points are generated by Latin hypercube design to construct the radial basis function metamodel,whose accuracy is improved with increasing number of sample points *** c-mean method and Gath-Geva clustering method are applied to divide the design space into several small interesting cluster spaces for low and high dimensional problems *** efficiency and accuracy are directly related to the design space,so unconcerned spaces are eliminated by the proposed reduction principle and two pseudo reduction *** reduction principle is developed to determine whether the current design space should be reduced and which space is *** first pseudo reduction algorithm improves the speed of clustering,while the second pseudo reduction algorithm ensures the design space to be *** several numerical benchmark functions,comparative studies with adaptive response surface method,approximated unimodal region elimination method and mode-pursuing sampling are carried *** optimization results reveal that this method captures the real global optimum for all the numerical benchmark *** the number of function evaluations show that the efficiency of this method is favorable especially for high dimensional *** on this global design optimization method,a design optimization of a lifting surface in high speed flow is carried out and this method saves about 10 h compared with genetic *** method possesses favorable performance on efficiency,robustness and capability of global

关键词： global optimization metamodel-based optimization reduction of design space fuzzy clustering

TW-Co-MFC:Two-Level Weighted Collaborative fuzzy clustering Based on Maximum Entropy for Multi-View Data

在线全文

学校读者我要写书评

暂无评论

Tsinghua Science and Technology 2021年第2期26卷 185-198页

作者： Jie Hu Yi Pan Tianrui Li Yan Yang School of Information Science and Technology Southwest Jiaotong UniversityChengdu 611756China Department of Computer Science Georgia State UniversityAtlantaGA 30302-3994USA

In recent years,multi-view clustering research has attracted considerable attention because of the rapidly growing demand for unsupervised analysis of multi-view data in practical *** the significant advances in multi-view clustering,two challenges still need to be addressed,i.e.,how to make full use of the consistent and complementary information in multiple views and how to discriminate the contributions of different views and features in the same view to efficiently reveal the latent cluster structure of multi-view data for *** this study,we propose a novel Two-level Weighted Collaborative Multi-view fuzzy clustering(TW-Co-MFC)approach to address the aforementioned *** TW-Co-MFC,a two-level weighting strategy is devised to measure the importance of views and features,and a collaborative working mechanism is introduced to balance the within-view clustering quality and the cross-view clustering *** an iterative optimization objective function based on the maximum entropy principle is designed for multi-view *** on real-world datasets show the effectiveness of the proposed approach.

关键词： multi-view clustering fuzzy clustering collaborative weighting maximum entropy

User preferences-aware recommendation for trustworthy cloud services based on fuzzy clustering

在线全文

学校读者我要写书评

暂无评论

Journal of Central South University 2015年第9期22卷 3495-3505页

作者：马华胡志刚 School of Software Central South University School of Information Science and Engineering Hunan International Economics University

The cloud computing has been growing over the past few years, and service providers are creating an intense competitive world of business. This proliferation makes it hard for new users to select a proper service among a large amount of service candidates. A novel user preferences-aware recommendation approach for trustworthy services is presented. For describing the requirements of new users in different application scenarios, user preferences are identified by usage preference, trust preference and cost preference. According to the similarity analysis of usage preference between consumers and new users, the candidates are selected, and these data about service trust provided by them are calculated as the fuzzy comprehensive evaluations. In accordance with the trust and cost preferences of new users, the dynamic fuzzy clusters are generated based on the fuzzy similarity computation. Then, the most suitable services can be selected to recommend to new users. The experiments show that this approach is effective and feasible, and can improve the quality of services recommendation meeting the requirements of new users in different scenario.

关键词： trustworthy service service recommendation user preferences-aware fuzzy clustering

Adding-Point Strategy for Reduced-Order Hypersonic Aerothermodynamics Modeling Based on fuzzy clustering

在线全文

学校读者我要写书评

暂无评论

Chinese Journal of Mechanical Engineering 2016年第5期29卷 983-991页

作者： CHEN Xin LIU Li ZHOU Sida YUE Zhenjiang Key Laboratory of Dynamics and Control of Flight Vehicle of Ministry of Education Beijing Institute of Technology School of Aerospace Engineering Beijing Institute of Technology

Reduced order models（ROMs） based on the snapshots on the CFD high-fidelity simulations have been paid great attention recently due to their capability of capturing the features of the complex geometries and flow configurations. To improve the efficiency and precision of the ROMs, it is indispensable to add extra sampling points to the initial snapshots, since the number of sampling points to achieve an adequately accurate ROM is generally unknown in prior, but a large number of initial sampling points reduces the parsimony of the ROMs. A fuzzy-clustering-based adding-point strategy is proposed and the fuzzy clustering acts an indicator of the region in which the precision of ROMs is relatively low. The proposed method is applied to construct the ROMs for the benchmark mathematical examples and a numerical example of hypersonic aerothermodynamics prediction for a typical control surface. The proposed method can achieve a 34.5% improvement on the efficiency than the estimated mean squared error prediction algorithm and shows same-level prediction accuracy.

关键词： reduced order model fuzzy clustering hypersonic aerothermodynamics adding-point strategy

Improved fuzzy clustering for image segmentation based on a low-rank prior

在线全文

学校读者我要写书评

暂无评论

Computational Visual Media 2021年第4期7卷 513-528页

作者： Xiaofeng Zhang Hua Wang Yan Zhang Xin Gao Gang Wang Caiming Zhang School of Information and Electrical Engineering Ludong UniversityYantai 264025China Shandong Province Key Lab of Digital Media Technology Shandong University of Finance and EconomicsJinan 250061China

Image segmentation is a basic problem in medical image analysis and useful for disease ***,the complexity of medical images makes image segmentation *** recent decades,fuzzy clustering algorithms have been preferred due to their simplicity and ***,they are sensitive to *** solve this problem,many algorithms using non-local information have been proposed,which perform well but are *** paper proposes an improved fuzzy clustering algorithm utilizing nonlocal self-similarity and a low-rank prior for image ***,cluster centers are initialized based on peak ***,a pixel correlation model between corresponding pixels is constructed,and similar pixel sets are *** improve efficiency and robustness,the proposed algorithm uses a novel objective function combining non-local information and a low-rank *** on synthetic images and medical images illustrate that the algorithm can improve efficiency greatly while achieving satisfactory results.

关键词： image segmentation fuzzy clustering nonlocal information low-rank prior medical images

Global Optimization Method Using SLE and Adaptive RBF Based on fuzzy clustering

在线全文

学校读者我要写书评

暂无评论

Chinese Journal of Mechanical Engineering 2012年第4期25卷 768-775页

作者： ZHU Huaguang LIU Li LONG Teng ZHAO Junfeng School of Aerospace Engineering Beijing Institute of Technology Beijing 100081 China

High fidelity analysis models,which are beneficial to improving the design quality,have been more and more widely utilized in the modern engineering design optimization ***,the high fidelity analysis models are so computationally expensive that the time required in design optimization is usually *** order to improve the efficiency of optimization involving high fidelity analysis models,the optimization efficiency can be upgraded through applying surrogates to approximate the computationally expensive models,which can greately reduce the computation *** efficient heuristic global optimization method using adaptive radial basis function（RBF） based on fuzzy clustering（ARFC） is *** this method,a novel algorithm of maximin Latin hypercube design using successive local enumeration（SLE） is employed to obtain sample points with good performance in both space-filling and projective uniformity properties,which does a great deal of good to metamodels *** method is adopted for constructing the metamodels,and with the increasing the number of sample points the approximation accuracy of RBF is gradually *** fuzzy c-means clustering method is applied to identify the reduced attractive regions in the original design *** numerical benchmark examples are used for validating the performance of *** results demonstrates that for most application examples the global optima are effectively obtained and comparison with adaptive response surface method（ARSM） proves that the proposed method can intuitively capture promising design regions and can efficiently identify the global or near-global design *** method improves the efficiency and global convergence of the optimization problems,and gives a new optimization strategy for engineering design optimization problems involving computationally expensive models.

关键词： global optimization Latin hypercube design radial basis function fuzzy clustering adaptive response surface method