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FHC: The fuzzy hyper-prototype clustering algorithm

INTERNATIONAL JOURNAL OF KNOWLEDGE-BASED AND INTELLIGENT ENGINEERING SYSTEMS 2012年第1期16卷 35-47页

作者： Liu, Jin Pham, Tuan D. Univ New South Wales Sch Engn & Informat Technol Canberra ACT Australia

We propose a fuzzy hyper-prototype clustering algorithm in this paper. This approach uses hyperplanes to represent the cluster centers in the fuzzy clustering. We present the formulation of fuzzy objective function and derive an iterative numerical algorithm for minimizing the objective function. Validations and comparisons are made between the proposed fuzzy clustering algorithm and existing fuzzy clustering methods on artificially generated data as well as on real world dataset include UCI dataset and gene expression dataset, the results show that the proposed method can give better performance in the above cases.

关键词： FHC fuzzy clustering algorithm hyper-prototype pattern classification

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Application of a Novel fuzzy clustering Method Based on Chaos Immune Evolutionary algorithm for Edge Detection in Image Processing

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中国高等学校学术文摘·机械工程 2006年第1期1卷 85-89页

作者： WANG Sun-an GUO Zi-long Shool of Mechanical Engineering Xi'an Jiaotong UniversityXi'an710049China

A novel fuzzy clustering method based on chaos immune evolutionary algorithm(CIEFCM)is presented to solve fuzzy edge detection problems in image *** CIEFCM,a tiny disturbance is added to a filial generation group using a chaos variable and the disturbance amplitude is adjusted step by step,which greatly improves the colony diversity ofthe immune evolution algorithm(IEA).The experimental results show that the method not only can correctly detect the fuzzy edge and exiguous edge but can evidently improve the searching efficiency of fuzzy clustering algorithm based on IEA.

关键词： immune evolutionary algorithm chaos search fuzzy clustering algorithm edge detection

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A Novel fuzzy clustering Method Based on Chaos Small-World algorithm for Image Edge Detection

A Novel Fuzzy Clustering Method Based on Chaos Small-World A...

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IEEE International Conference on Cybernetic Intelligent Systems (CIS 2008)

作者： Yuan, Mingxin Wang, Sun'an Chen, Naijian Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Shaanxi Peoples R China

ISBN: (纸本)9781424416738

To solve the fuzzy edge detection problems in image processing, a novel fuzzy clustering method based on chaos small-world algorithm (CSWFCM) is presented. The traditional fuzzy clustering method (FCM) is good at local searching capability, but it is sensitive to the initial value and easy to trap into local minimum value. The small-world algorithm (SWA), inspired by the mechanism of small-world phenomenon, is a novel global searching algorithm, which enables to enhance the diversity of the population and avoid trapping into local minimum value. However, the further capability of solving complicated problems is limited for its low efficiency of local short-range searching operator. In this paper, the chaos disturbance is utilized to improve the searching efficiency of SWA after local short-range search, and the chaos small-world algorithm (CSWA) is used to optimize the FCM in image edge detection. The simulation results show that the proposed algorithm can correctly detect the fuzzy and exiguous edges with higher convergence speed.

关键词： fuzzy clustering algorithm chaos optimization small-world algorithm edge detection

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fuzzy if-then rule generation based on neural network and clustering algorithm techniques

Fuzzy if-then rule generation based on neural network and cl...

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IEEE Region 10 Technical Conference on Computers, Communications, Control and Power Engineering

作者： Shi, Y Mizumoto, M Shi, P Kyushu Tokai Univ Sch Informat Sci Kumamoto 8628652 Japan

ISBN: (纸本)0780374908

This paper proposes a self-tuning method for fuzzy if-then rule generation based on neural network and clustering algorithm techniques. In the tuning approach, the initial parameters of fuzzy rules are roughly designed by Using a fuzzy clustering algorithim, and then fuzzy rules under fuzzy singleton-type reasoning are tuned by using a neuro-fuzzy learning algorithm. By this approach, the learning time can be reduced and the generated fuzzy rules are reasonable. and suitable for the identified system. Finally, identifying a nonlinear function shows the efficiency of. the tuning approach.

关键词： fuzzy rule generation neural network learning fuzzy clustering algorithm

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Research on personalized E-learning system using fuzzy set based clustering algorithm

Research on personalized E-learning system using fuzzy set b...

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7th International Conference on Computational Science (ICCS 2007)

作者： Lu, Feng Li, Xin Liu, Qingtang Yang, Zongkai Tan, Guoxin He, Tingting Huazhong Normal Univ Engn & Res Ctr Informat Technol Educ Wuhan 430079 Peoples R China Hunan Unive Sci & Engn Sci & Technol Educ Dept Yongzhou 425100 Peoples R China

ISBN: (纸本)9783540725879

Personalized service is becoming increasingly important, especially in E-learning field. Most personalized E-learning systems only take learners preferences, interests and browsing behaviors into consideration. These systems usually neglect considering whether the learners ability and the difficulty level of recommended learning materials are matched to each other or not. This paper proposes a personalized E-learning system using fuzzy set based clustering algorithm which considers both course materials' difficulty and learners' ability to provide appropriate learning stuffs for learners individually, to help learners learn more efficiently and effectively.

关键词： difficulty level learner ability fuzzy clustering algorithm

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Credit Default Risk Measurement and Statistical Analysis Based on Improved GRU Model

ENGINEERING REPORTS

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ENGINEERING REPORTS 2025年第2期7卷

作者： Yi, Zhifei Shandong Univ Finance & Econ Sch Stat & Math Jinan Peoples R China

With the increasing complexity of financial markets, credit defaults may not only affect the cash flow of enterprises, but also pose a threat to the stability of financial markets. Therefore, the management of credit default risk has become particularly important. This study constructs a new credit default risk assessment model by improving the gated recurrent unit algorithm and introducing Focal Loss function and fuzzy clustering algorithm. The new model can effectively capture market dynamics and nonlinear features, lessen the weight of easily classified samples, and accurately identify and eliminate redundant data through Pearson correlation analysis, thereby improving the precise measurement of default risk. The research findings indicate that the new model performs well in credit default risk assessment, with an accuracy rate of 96.53%, precision of 0.96, recall rate of 0.97, F1 value of 0.97, and all indicators reaching over 96%, significantly better than traditional Logistic and Copula models. In terms of the total time required for feature extraction, model training, and testing, the new model only takes 59 ms, which is 57 ms faster than the conventional Logistic algorithm, demonstrating the potential application of the new model in real-time risk monitoring. From this, the new model can not only accurately assess the default risk of credit, but also quickly complete statistical analysis. The new model can help financial institutions and enterprises reduce the proportion of non-performing assets, improve asset returns, protect the interests of investors, and provide a new analytical tool for real-time risk monitoring in the financial market.

关键词： credit default focal loss function fuzzy clustering algorithm gated recurrent unit algorithm risk assessment

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Application of computer wireless network database technology in information management

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JOURNAL OF INTELLIGENT SYSTEMS 2025年第1期34卷

作者： Zheng, Bin Beijing Open Univ Affiliated Coll Beijing 100000 Peoples R China

This article discusses the application of computer wireless network database technology in information management, aiming to solve the problem of low efficiency in information management by improving query efficiency and upload rate. Through standardized database technical operations, such as unified addition, deletion, query, and introduction, orderly adjustment and modification of internal big data are carried out to ensure that information transmission can be accurately reflected in the network structure to improve the quality of the system operation. This article uses a fuzzy clustering algorithm to evaluate the information management system comprehensively. It deeply analyzes the significant effects of database technology in improving user experience and enhancing system adaptability through experiments. The experimental results show that the new system is superior to the traditional information management system in data collection, upload efficiency, task management, query statistics, data monitoring, buffering effect, data encryption, access control, resource utilization, bandwidth utilization, data storage efficiency, scalability, reliability, and disaster recovery capability, user experience, and system adaptability. Specifically, the average response time of the new system is less than 2.5 s, the data upload rate reaches 146 Mbps, the user login success rate and the authentication accuracy rate are close to 100%, the resource utilization and network bandwidth utilization reach high levels in different periods, and the data storage efficiency is improved by 0.21. The system's scalability is improved by 0.29 with the support of database technology. These data show that computer wireless network database technology can significantly improve the efficiency and security of information management and effectively support the needs of high data volume and high-speed industries, such as finance, medical care, and e-commerce.

关键词： information management computer wireless network network database technology fuzzy clustering algorithm system suitability

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Identification of an Irrigation Station Using Hybrid fuzzy clustering algorithms Based on Particle Swarm Optimization 12

Identification of an Irrigation Station Using Hybrid Fuzzy C...

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International Multi-Conference on Systems, Signals and Devices (SSD)

作者： Chrouta, Jaouher Zaafouri, Abderrahmen Jemli, Mohamed ENSIT Res Unit Control Monitoring & Safety Syst C3S Tunis Tunisia

ISBN: (纸本)9781479917587

fuzzy c-means (FCM) and Gustafson-Kessel (GK) algorithms are the best popular fuzzy clustering techniques in terms of efficient, straightforward, and easy to implement. However, these algrithms are sensitive to initialization and easy to trap in the local minimum. The important issue is how to avoid getting a bad local minimum value to improve the cluster accuracy. In fact, the particle swarm algorithm is strong global searching ability which is based on swarm operation, it doesn't easily get into the local minimum and has a fast convergence speed. In order to overcome the weakness of traditional clustering algorithms and takes advantage of PSO, we integrate FCM and GK algorithms with fuzzy particle swarm algorithm (FCM-PSO and GK-PSO algorithms). In this paper, hybrid fuzzy clustering algorithms based on FCM, GK and PSO called FCM-PSO and GK-PSO are presented. A comparative study between the clustering algorithms is investigated to identify the parameter of irrigation station. Experimental results applied to the irrigation station show that the GK-PSO algorithm is more effective and robust compared to the other algorithms.

关键词： Irrigation Station fuzzy clustering algorithm non-linear systems T-S fuzzy model fuzzy C-Mean algorithm Particle Swarm Optimization

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A NEW fuzzy clustering algorithmS BASED ON TRANSFORMED DATA

A NEW FUZZY CLUSTERING ALGORITHMS BASED ON TRANSFORMED DATA

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International Conference on Machine Learning and Cybernetics

作者： Liu, Hsiang-Chuan Jeng, Bai-Cheng Wu, Der-Bang Lo, Yi-Hsiang Asia Univ Dept Bioinformat Taichung 41354 Taiwan Taichung Univ Grad Inst Educ Measurement Taichung 40306 Taiwan Taichung Univ Dept Math Educ Taichung 40306 Taiwan

ISBN: (纸本)9781424447053

The popular fuzzy c-means algorithm (FCM) is an objective function based clustering method. Hence, different objective function may lead to different results. The important issue is how to get a more compact and separable objective function to improve the cluster accuracy. The objective function of the well known improved algorithm, FCS, is a generalization of the FCM objective function by combining fuzzy within- and between-cluster variations. In this paper, considering a more separable data transformation, the improved new algorithm, "fuzzy Transformed C-Mean (FTCM)", is proposed. Three real data sets were applied to prove that the performance of the FTCM algorithm is better than the conventional FCM algorithm and the FCS algorithm.

关键词： fuzzy clustering algorithm FCM FCS FTCM

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A survey of fuzzy clustering validity evaluation methods

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INFORMATION SCIENCES 2022年 618卷 270-297页

作者： Wang, Hong -Yu Wang, Jie-Sheng Wang, Guan Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114044 Peoples R China

As an unsupervised learning method, clustering does not need to know prior knowledge of the datasets in advance. How determining the optimal number of clusters becomes an important method to judge the quality of clustering results. For fuzzy clustering algo-rithms, the introduction to fuzzy partition makes it more consistent with the structure of real datasets than hard clustering algorithms. Therefore, it is necessary to carry out the research on the validity evaluation methods of fuzzy clustering. At present, the research on fuzzy clustering validity mainly focuses on the fuzzy clustering validity index (FCVI) and the combined fuzzy clustering validity evaluation method (CFCVE). From these two aspects, this paper reviews fuzzy clustering validity functions and combined fuzzy clustering validity evaluation methods. Then FCVI and CFCVE are discussed in details from different points on fuzzy clustering validity functions, and the research status and con-struction strategies of different fuzzy clustering validity evaluation methods are analyzed. The accuracy and stability of each fuzzy clustering validity evaluation method are analyzed through comparative experiments. Finally, the paper summarizes the shortcomings and advantages of the current research on fuzzy clustering validity and looks forward to the research direction and improved methods of the evaluation methods.(c) 2022 Elsevier Inc. All rights reserved.

关键词： fuzzy clustering algorithm fuzzy clustering validity function Combined fuzzy clustering evaluation Optimal clustering number

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