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Proceedings 2015 8 International congress on Image and Signal Processing (cISP)

作者： Liao, Fan Zhao, chunxia Henan Univ TCM Zhengzhou Henan Peoples R China

ISBN: (纸本)9781467390989

The image features of pulmonary nodules in the cT image are inconspicuous, the shape and location is different. computer-aided detection system can increase the detection rate of lung nodules and reduce the error rate, which can assist the clinicians to distinguish between benign and malignant nodules. This paper presents an improved fuzzy c-means (FcM) method based on the human visual attention model. The simulation result shows the effects of the new method used in computer-aided diagnosis.

关键词： fuzzy c-means clustering algorithm human visual attention model computer-aided diagnosis

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Improving fuzzy c-means clustering algorithm based on a density-induced distance measure

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JOURNAL OF ENGINEERING-JOE 2014年第4期2014卷 137-139页

作者： Lu, chunhong Xiao, Shaoqing Gu, Xiaofeng Jiangnan Univ Dept Elect Engn Key Lab Adv Proc Control Light Ind Wuxi 214122 Peoples R China

The authors report an improved fuzzy c-means algorithm in comparison with the conventional one by employing a density-induced distance metric based on a novel calculation method of relative density degree. By using various synthetic and real data sets, the clustering performance of the proposed method is systematically studied and compared with that of the conventional one. The obtained results support the conclusion that this novel method does not only inherit good characteristics of the traditional one, but also possesses improved partitions.

关键词： fuzzy set theory pattern clustering synthetic data sets real data sets relative density degree density-induced distance measure fuzzy c-means clustering algorithm

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component-wise design method of fuzzy c-means clustering validity function based on cRITIc combination weighting

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JOURNAL OF SUPERcOMPUTING 2023年第13期79卷 14571-14601页

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

clustering validity function is an important research direction in clustering problems. Its idea is to specify the number of data clusters in advance so as to judge the optimal partition result on data sets. Studies have shown that no clustering validity function can handle all types of data, and its performance is not consistently better than other indices. Therefore, a component-wise design method of the fuzzy c-means (FcM) clustering validity function based on cRITIc combination weighting is proposed by adopting components for evaluating clustering performance. The weighting method combines expert weighting and the coefficient of variation method (cRITIc), arranges and combines six validity components with weights, and generates 55 different fuzzy clustering validity functions. These clustering validity functions are then tested on six typical UcI data sets, and the function with the simplest structure and best classification performance is selected through comparison. Finally, it is compared with seven typical clustering validity functions and four common combination clustering validity evaluation methods on eight UcI data sets. The simulation results demonstrate that the proposed validity function can achieve better classification results and determine the optimal cluster number for different data sets.

关键词： fuzzy c-means clustering algorithm clustering validity function cRITIc combination weighting component-wise design

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clustering-based reliability assessment of smart grids by fuzzy c-means algorithm considering direct cyber-physical interdependencies and system uncertainties

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SUSTAINABLE ENERGY GRIDS & NETWORKS 2022年 31卷

作者： Memari, Mehran Karimi, Ali Hashemi-Dezaki, Hamed Univ Kashan Fac Elect & Comp Engn Kashan Iran Univ West Bohemia UWB Fac Elect Engn Reg Innovat Ctr Elect Engn RICE Plzen 301614 Czech Republic

The steadily growing deployment of cyber systems in smart grids (SGs) has highlighted the impacts of cyber-physical interdependencies (cPIs). Although much attention has been paid to the reliability evaluation of SGs considering the system uncertainties and cPIs by Monte carlo simulation (McS), the computation time is one of the essential challenges of McS-based methods. This research tries to overcome the discussed challenge by developing a new clustering-based reliability evaluation method considering the direct cPIs (DcPIs) and stochastic behaviors of renewable distributed generation units (RDGUs) and the demand side. In the proposed method, the fuzzy c-means (FcM) clustering algorithm has been used to reduce the number of scenarios for uncertain parameters besides the DcPIs. Determining the appropriate alternatives for the number of clusters of stochastic parameters in various cases based on cyber network topologies, DG technologies, and the penetration levels of RDGUs is another contribution of this paper. Test results of applying the proposed method to an actual test system illustrate the advantages of the proposed clustering-based method. The comparison of the proposed method with McS shows the computation time could be reduced from 21658 s to 210 s (99%), while less than 1% EENS error appears. (c) 2022 Elsevier Ltd. All rights reserved.

关键词： Smart grid cyber-power interdependencies Reliability evaluation fuzzy c-means clustering algorithm Monte carlo simulation Uncertainty

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fuzzy c-means clustering Validity Function Based on Multiple clustering Performance Evaluation components

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INTERNATIONAL JOURNAL OF fuzzy SYSTEMS 2022年第4期24卷 1859-1887页

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

clustering is the process of grouping a set of physical or abstract objects into multiple similar objects. fuzzy c-means (FcM) clustering is one of the most widely used clustering methods, whose main research goal is to find the optimal clustering number of data sets, which is related to whether the data can be effectively divided. The study of clustering validity function is the process of evaluating the clustering quality and determining the optimal clustering number. Based on the idea of components, six cluster performance evaluation components are proposed to define compactness, variation, similarity, overlap and separation of data sets, respectively. Then a new validity function based on FcM clustering algorithm is synthesized by these six components. Finally, the proposed validity function and eight typical validity functions are compared on five artificial data sets and eight UcI data sets. The simulation results show that the proposed clustering validity function can evaluate the clustering results more effectively and determine the optimal clustering number of different data sets.

关键词： fuzzy c-means clustering algorithm clustering validity function clustering performance evaluation components

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Ratio component-wise design method of fuzzy c-means clustering validity function

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JOURNAL OF INTELLIGENT & fuzzy SYSTEMS 2022年第4期43卷 4691-4707页

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

fuzzy clustering is an important research field in pattern recognition, machine learning and image processing. The fuzzy c-means (FcM) clustering algorithm is one of the most common fuzzy clustering algorithms. However, it requires a given number of clusters in advance for accurate clustering of data sets, so it is necessary to put forward a better clustering validity index to verify the clustering results. This paper presents a ratio component-wise design method of clustering validity function based on FcM clustering method. By permutation and combination of six clustering validity components representing different meanings in the form of ratio, 49 different clustering validity functions are formed. Then, these functions are verified experimentally under six kinds of UcI data sets, and a clustering validity function with the simplest structure and the best classification effect is selected by comparison. Finally, this function is compared with seven traditional clustering validity functions on eight UcI data sets. The simulation results show that the proposed validity function can better verify the classification results and determine the optimal clustering number of different data sets.

关键词： Data mining fuzzy c-means clustering algorithm clustering validity function ratio component-wise design

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Optimal design of structures under earthquake loads using basic modal displacements method enhanced by fuzzy c-means clustering

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STRUcTURES 2021年 32卷 778-791页

作者： Balaghi, Sadegh Naseralavi, Seyed Sadegh Khojastehfar, Ehsan Vali e Asr Univ Rafsanjan Dept Civil Engn Rafsanjan Iran

The achievement of optimal design of structures against earthquake loading is investigated by evaluating the dynamic response. First, the paper presents an efficient preliminary technique called basic modal displacements (BMD) method to evaluate the dynamic history response. Second, a fuzzy version of BMD is developed as the premier method for prediction of peak response value of structures to be used within genetic algorithm (GA) optimization. In BMD the seismic response of a new design of structure is approximated by revising exact modal responses of the primary design of structure referred to as base model. In the other proposed method called clustered basic modal displacements (cBMD) the accuracy of BMD method to evaluate the maximum response is significantly improved by selection of different base models for created structures in the optimization process. In cBMD the base models are selected using fuzzy c-means clustering algorithm. Three structures are designed for optimal weight using GA. Results show that the proposed approach, (cBMD), is successful comparing to those found in the literature.

关键词： Optimization Dynamic analysis of structures Basis modal displacements fuzzy c-means clustering algorithm clustered basis modal displacements

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Semi-Supervised fuzzy c-means clustering Optimized by Simulated Annealing and Genetic algorithm for Fault Diagnosis of Bearings

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IEEE AccESS 2020年 8卷 181976-181987页

作者： Xiong, Jianbin Liu, Xi Zhu, Xingtong Zhu, Hongbin Li, Haiying Zhang, Qinghua Guangdong Polytech Normal Univ Sch Automat Guangzhou 510665 Peoples R China Guangdong Univ Petrochem Technol Sch Comp Maoming 525000 Peoples R China Guangdong Univ Technol Sch Automat Dept Automat Control Guangzhou 510006 Peoples R China Guangdong Maoming Agr & Forestry Tech Coll Dept Econ & Trade Maoming 525024 Peoples R China Guangdong Univ Petrochem Technol Guangdong Prov Petrochem Equipment Fault Diag PEF Maoming 525000 Peoples R China

As a popular clustering algorithms, fuzzy c-means (FcM) algorithm has been used in various fields, including fault diagnosis, machine learning. To overcome the sensitivity to outliers problem and the local minimum problem of the fuzzy c-means new algorithm is proposed based on the simulated annealing (SA) algorithm and the genetic algorithm (GA). The combined algorithm utilizes the simulated annealing algorithm due to its local search abilities. Thereby, problems associated with the genetic algorithm, such as its tendency to prematurely select optimal values, can be overcome, and genetic algorithm can be applied in fuzzy clustering analysis. Moreover, the new algorithm can solve other problems associated with the fuzzy clustering algorithm, which include initial clustering center value sensitivity and convergence to a local minimum. Furthermore, the simulation results can be used as classification criteria for identifying several types of bearing faults. compare with the dimensionless indexes, it shows that the mutual dimensionless indexes are more suitable for clustering algorithms. Finally, the experimental results show that the method adopted in this paper can improve the accuracy of clustering and accurately classify the bearing faults of rotating machinery.

关键词： clustering algorithms Genetic algorithms Fault diagnosis Indexes Machinery Simulated annealing Time-domain analysis Rotating machinery mutual dimensionless indexes fuzzy c-means clustering algorithm genetic algorithm simulated annealing algorithm

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An Improved fuzzy c-means clustering algorithm Based on Simulated Annealing

An Improved Fuzzy C-means Clustering Algorithm Based on Simu...

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10th International conference on fuzzy Systems and Knowledge Discovery (FSKD)

作者： Liu, Peiyu Duan, Linshan chi, Xuezhi Zhu, Zhenfang Shandong Normal Univ Dept Informat Sci & Engn Jinan 250014 Peoples R China Shandong Police Coll Jinan 250014 Peoples R China Shandong Jiatong Univ Sch Informat Sci & Elect Engn Jinan 250357 Peoples R China

ISBN: (纸本)9781467352536

fuzzy c-means clustering algorithm (FcM) is a widely used clustering algorithm, however it has its drawbacks: the initial number of clusters needs to be determined by the manual control according to the prior knowledge;the objective function ignores the disequilibrium problems among the sample attribute data. In view of these problems, this paper proposes a sample weighted FcM algorithm based on simulated annealing algorithm. It uses the simulated annealing algorithm which has an excellent ability of seeking global optimal solution to calculate the initial value of the number of clusters and makes certain weighting process on the clustering center function and the objective function. The experiment results show that this proposed algorithm has better classification accuracy and classification accuracy rate compared with FcM algorithm and the common sample weighted FcM clustering algorithms. Meanwhile, this algorithm needs not to be determined the initial value of clusters manually. The improved algorithm possesses the superiority and the actual application value.

关键词： Simulated Annealing fuzzy c-means clustering algorithm sample weighting

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Automatic detection of HFOs based on singular value decomposition and improved fuzzy c-means clustering for localization of seizure onset zones

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NEUROcOMPUTING 2020年 400卷 1-10页

作者： Wan, Xiongbo Fang, Zelin Wu, Min Du, Yuxiao China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China

This paper devises a new detector based on singular value decomposition (SVD) and improved fuzzy c-means (FcM) clustering for automatically detecting high-frequency oscillations (HFOs) that are used for localizing seizure onset zones (SOZs) in epilepsy. First, HFO candidates (HFOcs) are obtained by the root mean square method. Next, a time-frequency analysis method is applied to eliminate spikes from HFOcs, which consists of the Stockwell transform, SVD combined with the k-medoids clustering algorithm, Stockwell inverse transform, and threshold method. Then, four kinds of distinctive features, i.e. mean singular values, line lengths, power ratios and spectral centroid of the rest of HFOcs, are extracted and augmented as feature vectors. These vectors are used as the input of the improved FcM clustering algorithm optimized by the simulated annealing algorithm combined with the genetic algorithm. Finally, the localization of SOZs is accomplished based on the concentrations of the detected HFOs. The superiority of the devised detector over other five existing ones is demonstrated by comparing their localization performance. (c) 2020 Elsevier B.V. All rights reserved.

关键词： Epilepsy Seizure onset zones High-frequency oscillations Singular value decomposition fuzzy c-means clustering algorithm

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