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Intuitionistic fuzzy C-means clustering algorithms 认领 引用 被引量：30
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作者 Zeshui Xu Junjie Wu 《Journal of Systems Engineering and Electronics》 SCIE EI 2010年第4期580-590,共11页
Intuitionistic fuzzy sets(IFSs) are useful means to describe and deal with vague and uncertain data.An intuitionistic fuzzy C-means algorithm to cluster IFSs is developed.In each stage of the intuitionistic fuzzy C-me... Intuitionistic fuzzy sets(IFSs) are useful means to describe and deal with vague and uncertain data.An intuitionistic fuzzy C-means algorithm to cluster IFSs is developed.In each stage of the intuitionistic fuzzy C-means method the seeds are modified,and for each IFS a membership degree to each of the clusters is estimated.In the end of the algorithm,all the given IFSs are clustered according to the estimated membership degrees.Furthermore,the algorithm is extended for clustering interval-valued intuitionistic fuzzy sets(IVIFSs).Finally,the developed algorithms are illustrated through conducting experiments on both the real-world and simulated data sets. 展开更多
关键词 intuitionistic fuzzy set(IFS) intuitionistic fuzzy Cmeans algorithm clustering interval-valued intuitionistic fuzzy set(IVIFS).
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New two-dimensional fuzzy C-means clustering algorithm for image segmentation 认领 引用 被引量：11
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作者 周鲜成 申群太 刘利枚 《Journal of Central South University of Technology》 2008年第6期882-887,共6页
To solve the problem of poor anti-noise performance of the traditional fuzzy C-means (FCM) algorithm in image segmentation, a novel two-dimensional FCM clustering algorithm for image segmentation was proposed. In this... To solve the problem of poor anti-noise performance of the traditional fuzzy C-means (FCM) algorithm in image segmentation, a novel two-dimensional FCM clustering algorithm for image segmentation was proposed. In this method, the image segmentation was converted into an optimization problem. The fitness function containing neighbor information was set up based on the gray information and the neighbor relations between the pixels described by the improved two-dimensional histogram. By making use of the global searching ability of the predator-prey particle swarm optimization, the optimal cluster center could be obtained by iterative optimization, and the image segmentation could be accomplished. The simulation results show that the segmentation accuracy ratio of the proposed method is above 99%. The proposed algorithm has strong anti-noise capability, high clustering accuracy and good segment effect, indicating that it is an effective algorithm for image segmentation. 展开更多
关键词 image segmentation fuzzy C-means clustering particle swarm optimization two-dimensional histogram
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A Fixed Suppressed Rate Selection Method for Suppressed Fuzzy C-Means Clustering Algorithm 认领 引用 被引量：3
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作者 Jiulun Fan Jing Li 《Applied Mathematics》 2014年第8期1275-1283,共9页
Suppressed fuzzy c-means (S-FCM) clustering algorithm with the intention of combining the higher speed of hard c-means clustering algorithm and the better classification performance of fuzzy c-means clustering algorit... Suppressed fuzzy c-means (S-FCM) clustering algorithm with the intention of combining the higher speed of hard c-means clustering algorithm and the better classification performance of fuzzy c-means clustering algorithm had been studied by many researchers and applied in many fields. In the algorithm, how to select the suppressed rate is a key step. In this paper, we give a method to select the fixed suppressed rate by the structure of the data itself. The experimental results show that the proposed method is a suitable way to select the suppressed rate in suppressed fuzzy c-means clustering algorithm. 展开更多
关键词 Hard C-Means Clustering Algorithm Fuzzy C-Means Clustering Algorithm Suppressed Fuzzy C-Means Clustering Algorithm Suppressed Rate
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Deep Fuzzy C-Means Clustering in a Federated Heterogeneous Scenario 认领 引用
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作者 Longmei Li Wei Lu Witold Pedrycz 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第3期633-646,共14页
In federated deep Fuzzy C-Means(FCM)clustering,conventional federated averaging(FedAvg)struggles with nonindependent and identically distributed(non-IID)data and dynamic device participation,leading to model drift and... In federated deep Fuzzy C-Means(FCM)clustering,conventional federated averaging(FedAvg)struggles with nonindependent and identically distributed(non-IID)data and dynamic device participation,leading to model drift and performance degradation during global aggregation.To address this challenge,we propose FedFCD,a federated deep FCM clustering method featuring a novel aggregation mechanism.FedFCD equips each client with a hybrid architecture comprising a contrastive autoencoder(CtAE)and an FCM network(FCMNet),which collaboratively learn stable low-dimensional embeddings and refine soft clustering assignments iteratively.At the server side,we design a two-phase aggregation strategy integrating Bayesian ensemble learning and knowledge distillation(KD).First,the Bayesian aggregation mechanism probabilistically fuses heterogeneous local models'inferences into a consensus assignment by treating each client's model as a candidate hypothesis,thereby constructing a posterior distribution over the global model space through iterative evidence accumulation.Subsequently,dual-source distillation harmonizes pseudo-labels derived from the Bayesian consensus with ground-truth labels from limited shared data,enabling the global model to align its predictions with both semantic anchors and aggregated soft assignments while preserving privacy through distillation loss.Comparative experiments on benchmark datasets demonstrate that Fed FCD outperforms baseline methods in clustering accuracy and exhibits enhanced stability under varying conditions,including data heterogeneity,device numbers,and device dropout. 展开更多
关键词 Bayesian inference data distillation deep embedding clustering federated learning fuzzy C-means(FCM) model aggregation
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Fuzzy C-Means Clustering-Driven Pooling for Robust and Generalizable Convolutional Neural Networks 认领 引用
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作者 Seunggyu Byeon Jung-hun Lee Jong-Deok Kim 《Computers, Materials & Continua》 SCIE EI 2026年第5期579-604,共26页
This paper introduces a fuzzy C-means-based pooling layer for convolutional neural networks that explicitly models local uncertainty and ambiguity.Conventional pooling operations,such as max and average,apply rigid ag... This paper introduces a fuzzy C-means-based pooling layer for convolutional neural networks that explicitly models local uncertainty and ambiguity.Conventional pooling operations,such as max and average,apply rigid aggregation and often discard fine-grained boundary information.In contrast,our method computes soft membershipswithin each receptive field and aggregates cluster-wise responses throughmembership-weighted pooling,thereby preserving informative structure while reducing dimensionality.Being differentiable,the proposed layer operates as standard two-dimensional pooling.We evaluate our approach across various CNN backbones and open datasets,including CIFAR-10/100,STL-10,LFW,and ImageNette,and further probe small training set restrictions on MNIST and Fashion-MNIST.In these settings,the proposed pooling consistently improves accuracy and weighted F1 over conventional baselines,with particularly strong gains when training data are scarce.Even with less than 1%of the training set,ourmethodmaintains reliable performance,indicating improved sample efficiency and robustness to noisy or ambiguous local patterns.Overall,integrating soft memberships into the pooling operator provides a practical and generalizable inductive bias that enhances robustness and generalization in modern CNN pipelines. 展开更多
关键词 Fuzzy logic fuzzy c-means clustering membership-based pooling convolutional neural networks downsampling feature extraction
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Grouped machine learning methods for predicting rock mass parameters in a tunnel boring machine-driven tunnel based on fuzzy C-means clustering 认领 引用
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作者 Ruirui Wang Yaodong Ni +1 位作者 Lingli Zhang Boyang Gao 《Deep Underground Science and Engineering》 EI CAS CSCD 2025年第1期55-71,共17页
To guarantee safe and efficient tunneling of a tunnel boring machine(TBM),rapid and accurate judgment of the rock mass condition is essential.Based on fuzzy C-means clustering,this paper proposes a grouped machine lea... To guarantee safe and efficient tunneling of a tunnel boring machine(TBM),rapid and accurate judgment of the rock mass condition is essential.Based on fuzzy C-means clustering,this paper proposes a grouped machine learning method for predicting rock mass parameters.An elaborate data set on field rock mass is collected,which also matches field TBM tunneling.Meanwhile,target stratum samples are divided into several clusters by fuzzy C-means clustering,and multiple submodels are trained by samples in different clusters with the input of pretreated TBM tunneling data and the output of rock mass parameter data.Each testing sample or newly encountered tunneling condition can be predicted by multiple submodels with the weight of the membership degree of the sample to each cluster.The proposed method has been realized by 100 training samples and verified by 30 testing samples collected from the C1 part of the Pearl Delta water resources allocation project.The average percentage error of uniaxial compressive strength and joint frequency(Jf)of the 30 testing samples predicted by the pure back propagation(BP)neural network is 13.62%and 12.38%,while that predicted by the BP neural network combined with fuzzy C-means is 7.66%and6.40%,respectively.In addition,by combining fuzzy C-means clustering,the prediction accuracies of support vector regression and random forest are also improved to different degrees,which demonstrates that fuzzy C-means clustering is helpful for improving the prediction accuracy of machine learning and thus has good applicability.Accordingly,the proposed method is valuable for predicting rock mass parameters during TBM tunneling. 展开更多
关键词 fuzzy C-means clustering machine learning rock mass parameter tunnel boring machine
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Fast global kernel fuzzy c-means clustering algorithm for consonant/vowel segmentation of speech signal 认领 引用 被引量：3
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作者 Xian ZANG Felipe P. VISTA IV Kil To CHONG 《Journal of Zhejiang University-Science C(Computers and Electronics)》 2014年第7期551-563,共13页
We propose a novel clustering algorithm using fast global kernel fuzzy c-means-F(FGKFCM-F), where F refers to kernelized feature space. This algorithm proceeds in an incremental way to derive the near-optimal solution... We propose a novel clustering algorithm using fast global kernel fuzzy c-means-F(FGKFCM-F), where F refers to kernelized feature space. This algorithm proceeds in an incremental way to derive the near-optimal solution by solving all intermediate problems using kernel-based fuzzy c-means-F(KFCM-F) as a local search procedure. Due to the incremental nature and the nonlinear properties inherited from KFCM-F, this algorithm overcomes the two shortcomings of fuzzy c-means(FCM): sen- sitivity to initialization and inability to use nonlinear separable data. An accelerating scheme is developed to reduce the compu-tational complexity without significantly affecting the solution quality. Experiments are carried out to test the proposed algorithm on a nonlinear artificial dataset and a real-world dataset of speech signals for consonant/vowel segmentation. Simulation results demonstrate the effectiveness of the proposed algorithm in improving clustering performance on both types of datasets. 展开更多
关键词 Fuzzy c-means clustering Kernel method Global optimization Consonant/vowel segmentation
A New Method of Wind Turbine Bearing Fault Diagnosis Based on Multi-Masking Empirical Mode Decomposition and Fuzzy C-Means Clustering 认领 引用 被引量：13
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作者 Yongtao Hu Shuqing Zhang +3 位作者 Anqi Jiang Liguo Zhang Wanlu Jiang Junfeng Li 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2019年第3期156-167,共12页
Based on Multi-Masking Empirical Mode Decomposition (MMEMD) and fuzzy c-means (FCM) clustering, a new method of wind turbine bearing fault diagnosis FCM-MMEMD is proposed, which can determine the fault accurately and ... Based on Multi-Masking Empirical Mode Decomposition (MMEMD) and fuzzy c-means (FCM) clustering, a new method of wind turbine bearing fault diagnosis FCM-MMEMD is proposed, which can determine the fault accurately and timely. First, FCM clustering is employed to classify the data into different clusters, which helps to estimate whether there is a fault and how many fault types there are. If fault signals exist, the fault vibration signals are then demodulated and decomposed into different frequency bands by MMEMD in order to be analyzed further. In order to overcome the mode mixing defect of empirical mode decomposition (EMD), a novel method called MMEMD is proposed. It is an improvement to masking empirical mode decomposition (MEMD). By adding multi-masking signals to the signals to be decomposed in different levels, it can restrain low-frequency components from mixing in highfrequency components effectively in the sifting process and then suppress the mode mixing. It has the advantages of easy implementation and strong ability of suppressing modal mixing. The fault type is determined by Hilbert envelope finally. The results of simulation signal decomposition showed the high performance of MMEMD. Experiments of bearing fault diagnosis in wind turbine bearing fault diagnosis proved the validity and high accuracy of the new method. 展开更多
关键词 Wind turbine bearing faults diagnosis Multi-masking empirical mode decomposition (MMEMD) Fuzzy c-mean (FCM) clustering
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Power interconnected system clustering with advanced fuzzy C-mean algorithm 认领 引用 被引量：6
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作者 王洪梅 KIM Jae-Hyung +2 位作者 JUNG Dong-Yean LEE Sang-Min LEE Sang-Hyuk 《Journal of Central South University》 SCIE EI CAS 2011年第1期190-195,共6页
An advanced fuzzy C-mean （FCM） algorithm was proposed for the efficient regional clustering of multi-nodes interconnected systems. Due to various locational prices and regional coherencies for each node and point, m... An advanced fuzzy C-mean （FCM） algorithm was proposed for the efficient regional clustering of multi-nodes interconnected systems. Due to various locational prices and regional coherencies for each node and point, modified similarity measure was considered to gather nodes having similar characteristics. The similarity measure was needed to contain locafi0nal prices as well as regional coherency. In order to consider the two properties simultaneously, distance measure of fuzzy C-mean algorithm had to be modified. Regional clustering algorithm for interconnected power systems was designed based on the modified fuzzy C-mean algorithm. The proposed algorithm produces proper classification for the interconnected power system and the results are demonstrated in the example of IEEE 39-bus interconnected electricity system. 展开更多
关键词 fuzzy C-mean similarity measure distance measure interconnected system clustering
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Fuzzy c-means text clustering based on topic concept sub-space 认领 引用 被引量：3
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作者 吉翔华 陈超 +1 位作者 邵正荣 俞能海 《Journal of Southeast University(English Edition)》 EI CAS 2007年第3期439-442,共4页
To improve the accuracy of text clustering, fuzzy c-means clustering based on topic concept sub-space （TCS2FCM） is introduced for classifying texts. Five evaluation functions are combined to extract key phrases. Con... To improve the accuracy of text clustering, fuzzy c-means clustering based on topic concept sub-space （TCS2FCM） is introduced for classifying texts. Five evaluation functions are combined to extract key phrases. Concept phrases, as well as the descriptions of final clusters, are presented using WordNet origin from key phrases. Initial centers and membership matrix are the most important factors affecting clustering performance. Orthogonal concept topic sub-spaces are built with the topic concept phrases representing topics of the texts and the initialization of centers and the membership matrix depend on the concept vectors in sub-spaces. The results show that, different from random initialization of traditional fuzzy c-means clustering, the initialization related to text content contributions can improve clustering precision. 展开更多
关键词 TCS2FCM topic concept space fuzzy c-means clustering text clustering
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Watershed classification by remote sensing indices: A fuzzy c-means clustering approach 认领 引用 被引量：10
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作者 Bahram CHOUBIN Karim SOLAIMANI +1 位作者 Mahmoud HABIBNEJAD ROSHAN Arash MALEKIAN 《Journal of Mountain Science》 SCIE CSCD 2017年第10期2053-2063,共11页
Determining the relatively similar hydrological properties of the watersheds is very crucial in order to readily classify them for management practices such as flood and soil erosion control. This study aimed to ident... Determining the relatively similar hydrological properties of the watersheds is very crucial in order to readily classify them for management practices such as flood and soil erosion control. This study aimed to identify homogeneous hydrological watersheds using remote sensing data in western Iran. To achieve this goal, remote sensing indices including SAVI, LAI, NDMI, NDVI and snow cover, were extracted from MODIS data over the period 2000 to 2015. Then, a fuzzy method was used to clustering the watersheds based on the extracted indices. A fuzzy c-mean(FCM) algorithm enabled to classify 38 watersheds in three homogeneous groups.The optimal number of clusters was determined through evaluation of partition coefficient, partition entropy function and trial and error. The results indicated three homogeneous regions identified by the fuzzy c-mean clustering and remote sensing product which are consistent with the variations of topography and climate of the study area. Inherently,the grouped watersheds have similar hydrological properties and are likely to need similar management considerations and measures. 展开更多
关键词 Karkheh watershed Fuzzy c-means clustering Watershed classification Homogeneous sub-watersheds
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ALLIED FUZZY c-MEANS CLUSTERING MODEL 认领 引用 被引量：2
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作者 武小红 周建江 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2006年第3期208-213,共6页
A novel model of fuzzy clustering, i.e. an allied fuzzy c means （AFCM） model is proposed based on the combination of advantages of fuzzy c means （FCM） and possibilistic c means （PCM） clustering. PCM is sensitive... A novel model of fuzzy clustering, i.e. an allied fuzzy c means （AFCM） model is proposed based on the combination of advantages of fuzzy c means （FCM） and possibilistic c means （PCM） clustering. PCM is sensitive to initializations and often generates coincident clusters. AFCM overcomes this shortcoming and it is an ex tension of PCM. Membership and typicality values can be simultaneously produced in AFCM. Experimental re- suits show that noise data can be well processed, coincident clusters are avoided and clustering accuracy is better. 展开更多
关键词 fuzzy c-means clustering possibilistic c means clustering allied fuzzy c-means clustering
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Hybrid Clustering Using Firefly Optimization and Fuzzy C-Means Algorithm 认领 引用
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作者 Krishnamoorthi Murugasamy Kalamani Murugasamy 《Circuits and Systems》 2016年第9期2339-2348,共10页
Classifying the data into a meaningful group is one of the fundamental ways of understanding and learning the valuable information. High-quality clustering methods are necessary for the valuable and efficient analysis... Classifying the data into a meaningful group is one of the fundamental ways of understanding and learning the valuable information. High-quality clustering methods are necessary for the valuable and efficient analysis of the increasing data. The Firefly Algorithm (FA) is one of the bio-inspired algorithms and it is recently used to solve the clustering problems. In this paper, Hybrid F-Firefly algorithm is developed by combining the Fuzzy C-Means (FCM) with FA to improve the clustering accuracy with global optimum solution. The Hybrid F-Firefly algorithm is developed by incorporating FCM operator at the end of each iteration in FA algorithm. This proposed algorithm is designed to utilize the goodness of existing algorithm and to enhance the original FA algorithm by solving the shortcomings in the FCM algorithm like the trapping in local optima and sensitive to initial seed points. In this research work, the Hybrid F-Firefly algorithm is implemented and experimentally tested for various performance measures under six different benchmark datasets. From the experimental results, it is observed that the Hybrid F-Firefly algorithm significantly improves the intra-cluster distance when compared with the existing algorithms like K-means, FCM and FA algorithm. 展开更多
关键词 Clustering Optimization K-Means Fuzzy C-Means Firefly Algorithm F-Firefly
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A weighted fuzzy C-means clustering method for hardness prediction 认领 引用
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作者 Yuan Liu Shi-zhong Wei 《Journal of Iron and Steel Research International》 SCIE EI CAS CSCD 2023年第1期176-191,共16页
The hardness prediction model was established by support vector regression(SVR).In order to avoid exaggerating the contribution of very tiny alloying elements,a weighted fuzzy C-means(WFCM)algorithm was proposed for d... The hardness prediction model was established by support vector regression(SVR).In order to avoid exaggerating the contribution of very tiny alloying elements,a weighted fuzzy C-means(WFCM)algorithm was proposed for data clustering using improved Mahalanobis distance based on random forest importance values,which could play a full role of important features and avoid clustering center overlap.The samples were divided into two classes.The top 10 features of each class were selected to form two feature subsets for better performance of the model.The dimension and dispersion of features decreased in such feature subsets.Comparing four machine learning algorithms,SVR had the best performance and was chosen to modeling.The hyper-parameters of the SVR model were optimized by particle swarm optimization.The samples in validation set were classified according to minimum distance of sample to clustering centers,and then the SVR model trained by feature subset of corresponding class was used for prediction.Compared with the feature subset of original data set,the predicted values of model trained by feature subsets of classified samples by WFCM had higher correlation coefficient and lower root mean square error.It indicated that WFCM was an effective method to reduce the dispersion of features and improve the accuracy of model. 展开更多
关键词 Hardness prediction Weighted fuzzy C-means algorithm Feature selection Particle swarm optimization Support vector regression Dispersion reduction
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Diabetic Retinopathy Diagnosis Using ResNet with Fuzzy Rough C-Means Clustering 认领 引用 被引量：2
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作者 R.S.Rajkumar A.Grace Selvarani 《Computer Systems Science & Engineering》 SCIE EI 2022年第8期509-521,共13页
Diabetic Retinopathy(DR)is a vision disease due to the long-term prevalenceof Diabetes Mellitus.It affects the retina of the eye and causes severedamage to the vision.If not treated on time it may lead to permanent vi... Diabetic Retinopathy(DR)is a vision disease due to the long-term prevalenceof Diabetes Mellitus.It affects the retina of the eye and causes severedamage to the vision.If not treated on time it may lead to permanent vision lossin diabetic patients.Today’s development in science has no medication to cureDiabetic Retinopathy.However,if diagnosed at an early stage it can be controlledand permanent vision loss can be avoided.Compared to the diabetic population,experts to diagnose Diabetic Retinopathy are very less in particular to local areas.Hence an automatic computer-aided diagnosis for DR detection is necessary.Inthis paper,we propose an unsupervised clustering technique to automatically clusterthe DR into one of its five development stages.The deep learning based unsupervisedclustering is made to improve itself with the help of fuzzy rough c-meansclustering where cluster centers are updated by fuzzy rough c-means clusteringalgorithm during the forward pass and the deep learning model representationsare updated by Stochastic Gradient Descent during the backward pass of training.The proposed method was implemented using python and the results were takenon DGX server with Tesla V100 GPU cards.An experimental result on the publicallyavailable Kaggle dataset shows an overall accuracy of 88.7%.The proposedmodel improves the accuracy of DR diagnosis compared to the existingunsupervised algorithms like k-means,FCM,auto-encoder,and FRCM withalexnet. 展开更多
关键词 Diabetic retinopathy detection diabetic retinopathy diagnosis fuzzy rough c-means clustering unsupervised CNN clustering
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A New Integrated Fuzzifier Evaluation and Selection (NIFEs) Algorithm for Fuzzy Clustering 认领 引用 被引量：1
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作者 Chanpaul Jin Wang Hua Fang +2 位作者 Sun Kim Ann Moormann Honggang Wang 《Journal of Applied Mathematics and Physics》 2015年第7期802-807,共6页
Fuzzy C-means (FCM) is simple and widely used for complex data pattern recognition and image analyses. However, selecting an appropriate fuzzifier (m) is crucial in identifying an optimal number of patterns and achiev... Fuzzy C-means (FCM) is simple and widely used for complex data pattern recognition and image analyses. However, selecting an appropriate fuzzifier (m) is crucial in identifying an optimal number of patterns and achieving higher clustering accuracy, which few studies have investigated. Built upon two existing methods on selecting fuzzifier, we developed an integrated fuzzifier evaluation and selection algorithm and tested it using real datasets. Our findings indicate that the consistent optimal number of clusters can be learnt from testing different fuzzifiers for each dataset and the fuzzifier with the lowest value for this consistency should be selected for clustering. Our evaluation also shows that the fuzzifier impacts the clustering accuracy. For longitudinal data with missing values, m = 2 could be an empirical rule to start fuzzy clustering, and the best clustering accuracy was achieved for tested data, especially using our multiple-imputation based fuzzy clustering. 展开更多
关键词 Fuzzifier Fuzzy C-Means Multiple Imputation-Based Fuzzy Clustering (MIFuzzy) Missing Data Longitudinal Data
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Advanced Fuzzy C-Means Algorithm Based on Local Density and Distance 认领 引用 被引量：4
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作者 Shaochun PANG Yijie +1 位作者 SHAO Sen JIANG Keyuan 《Journal of Shanghai Jiaotong university(Science)》 EI 2018年第5期636-642,共7页
This paper presents an advanced fuzzy C-means(FCM) clustering algorithm to overcome the weakness of the traditional FCM algorithm, including the instability of random selecting of initial center and the limitation of ... This paper presents an advanced fuzzy C-means(FCM) clustering algorithm to overcome the weakness of the traditional FCM algorithm, including the instability of random selecting of initial center and the limitation of the data separation or the size of clusters. The advanced FCM algorithm combines the distance with density and improves the objective function so that the performance of the algorithm can be improved. The experimental results show that the proposed FCM algorithm requires fewer iterations yet provides higher accuracy than the traditional FCM algorithm. The advanced algorithm is applied to the influence of stars' box-office data, and the classification accuracy of the first class stars achieves 92.625%. 展开更多
关键词 objective function clustering center fuzzy C-means （FCM） clustering algorithm degree of member-ship
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Relative-Density-Viewpoint-Based Weighted Kernel Fuzzy Clustering 认领 引用 被引量：1
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作者 Yuhan Xia Xu Li +2 位作者 Ye Liu Wenbo Zhou Yiming Tang 《Computers, Materials & Continua》 SCIE EI 2025年第7期625-651,共27页
Applying domain knowledge in fuzzy clustering algorithms continuously promotes the development of clustering technology.The combination of domain knowledge and fuzzy clustering algorithms has some problems,such as ini... Applying domain knowledge in fuzzy clustering algorithms continuously promotes the development of clustering technology.The combination of domain knowledge and fuzzy clustering algorithms has some problems,such as initialization sensitivity and information granule weight optimization.Therefore,we propose a weighted kernel fuzzy clustering algorithm based on a relative density view(RDVWKFC).Compared with the traditional density-based methods,RDVWKFC can capture the intrinsic structure of the data more accurately,thus improving the initial quality of the clustering.By introducing a Relative Density based Knowledge Extraction Method(RDKM)and adaptive weight optimization mechanism,we effectively solve the limitations of view initialization and information granule weight optimization.RDKM can accurately identify high-density regions and optimize the initialization process.The adaptive weight mechanism can reduce noise and outliers’interference in the initial cluster centre selection by dynamically allocating weights.Experimental results on 14 benchmark datasets show that the proposed algorithm is superior to the existing algorithms in terms of clustering accuracy,stability,and convergence speed.It shows adaptability and robustness,especially when dealing with different data distributions and noise interference.Moreover,RDVWKFC can also show significant advantages when dealing with data with complex structures and high-dimensional features.These advancements provide versatile tools for real-world applications such as bioinformatics,image segmentation,and anomaly detection. 展开更多
关键词 Fuzzy clustering fuzzy c-means feature weighting information granule
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Research of Improved Fuzzy c-means Algorithm Based on a New Metric Norm 认领 引用 被引量：2
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作者 毛力 宋益春 +2 位作者 李引 杨弘 肖炜 《Journal of Shanghai Jiaotong university(Science)》 EI 2015年第1期51-55,共5页
For the question that fuzzy c-means(FCM)clustering algorithm has the disadvantages of being too sensitive to the initial cluster centers and easily trapped in local optima,this paper introduces a new metric norm in FC... For the question that fuzzy c-means(FCM)clustering algorithm has the disadvantages of being too sensitive to the initial cluster centers and easily trapped in local optima,this paper introduces a new metric norm in FCM and particle swarm optimization(PSO)clustering algorithm,and proposes a parallel optimization algorithm using an improved fuzzy c-means method combined with particle swarm optimization(AF-APSO).The experiment shows that the AF-APSO can avoid local optima,and get the best fitness and clustering performance significantly. 展开更多
关键词 fuzzy c-means(FCM) particle swarm optimization(PSO) clustering algorithm new metric norm
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NEW SHADOWED C-MEANS CLUSTERING WITH FEATURE WEIGHTS 认领 引用 被引量：3
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作者 王丽娜 王建东 姜坚 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2012年第3期273-283,共11页
Partition-based clustering with weighted feature is developed in the framework of shadowed sets. The objects in the core and boundary regions, generated by shadowed sets-based clustering, have different impact on the ... Partition-based clustering with weighted feature is developed in the framework of shadowed sets. The objects in the core and boundary regions, generated by shadowed sets-based clustering, have different impact on the prototype of each cluster. By integrating feature weights, a formula for weight calculation is introduced to the clustering algorithm. The selection of weight exponent is crucial for good result and the weights are updated iteratively with each partition of clusters. The convergence of the weighted algorithms is given, and the feasible cluster validity indices of data mining application are utilized. Experimental results on both synthetic and real-life numerical data with different feature weights demonstrate that the weighted algorithm is better than the other unweighted algorithms. 展开更多
关键词 fuzzy C-means shadowed sets shadowed C-means feature weights cluster validity index
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