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Application of Fuzzy C-mean Cluster Algorithm on Clutter Tracking 被引量:2
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作者 张弓 朱兆达 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2002年第1期44-48,共5页
This paper introduces a clutter tracking technique used forairborne PD radar. Combining the clutter feature of the airborne PDradar and characteristic of fuzzy C-means clustering algorithm, theauthors apply this algor... This paper introduces a clutter tracking technique used forairborne PD radar. Combining the clutter feature of the airborne PDradar and characteristic of fuzzy C-means clustering algorithm, theauthors apply this algorithm to the clutter tracking, and present theflow chart. A method of defining the fuzzy membership function isalso proposed. The algorithm has been verified to be suc- cessful inseveral typical experiments. 展开更多
关键词 PD radar clutter tracking image processing fuzzy c-mean
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An Airborne Radar Clutter Tracking Algorithm Based on Multifractal and Fuzzy C-Mean Cluster 被引量:3
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作者 Wei Zhang Sheng-Lin Yu Gong Zhang 《Journal of Electronic Science and Technology of China》 2007年第2期159-162,共4页
For an airborne Iookdown radar, clutter power often changes dynamically about 80 dB with wide distributions as the platform moves. Therefore, clutter tracking techniques are required to guide the selection of const fa... For an airborne Iookdown radar, clutter power often changes dynamically about 80 dB with wide distributions as the platform moves. Therefore, clutter tracking techniques are required to guide the selection of const false alarm rate (CFAR) schemes. In this work, clutter tracking is done in image domain and an algorithm combining multifractal and fuzzy C-mean (FCM) cluster is proposed. The clutter with large dynamic distributions in power density is converted to steady distributions of multifractal exponents by the multifractal transformation with the optimum moment. Then, later, the main lobe and side lobe are tracked from the multifractal exponents by FCM clustering method. 展开更多
关键词 Clutter tracking MULTIFRACTAL fuzzy Cmean (FCM) cluster image processing texture segmentation.
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Gene Coding Sequence Identification Using Kernel Fuzzy C-Mean Clustering and Takagi-Sugeno Fuzzy Model
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作者 Tianlei Zang Kai Liao +2 位作者 Zhongmin Sun Zhengyou He Qingquan Qian 《国际计算机前沿大会会议论文集》 2015年第1期78-79,共2页
Sequence analysis technology under big data provides unprecedented opportunities for modern life science. A novel gene coding sequence identification method is proposed in this paper. Firstly, an improved short-time F... Sequence analysis technology under big data provides unprecedented opportunities for modern life science. A novel gene coding sequence identification method is proposed in this paper. Firstly, an improved short-time Fourier transform algorithm based on Morlet wavelet is applied to extract the power spectrum of DNA sequence. Then, threshold value determination method based on kernel fuzzy C-mean clustering is used to combine Signal to Noise Ratio (SNR) data of exon and intron into a sequence, classify the sequence into two types, calculate the weighted sum of two SNR clustering centers obtained and the discrimination threshold value. Finally, exon interval endpoint identification algorithm based on Takagi-Sugeno fuzzy identification model is presented to train Takagi-Sugeno model, optimize model parameters with Levenberg-Marquardt least square method, complete model and determine fuzzy rule. To verify the effectiveness of the proposed method, example tests are conducted on typical gene sequence sample data. 展开更多
关键词 gene IDENTIFICATION power spectrum analysis THRESHOLD value determination KERNEL fuzzy c-mean clustering TAKAGI-SUGENO fuzzy IDENTIFICATION
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Fuzzy C-Mean Clustering Based: LEO Satellite Handover
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作者 Syed Umer Bukhaxi Liwei Yu +2 位作者 Xiao qiang Di Chunyi Chen Xu Liu 《国际计算机前沿大会会议论文集》 2018年第1期26-26,共1页
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Unknown DDoS Attack Detection with Fuzzy C-Means Clustering and Spatial Location Constraint Prototype Loss
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作者 Thanh-Lam Nguyen HaoKao +2 位作者 Thanh-Tuan Nguyen Mong-Fong Horng Chin-Shiuh Shieh 《Computers, Materials & Continua》 SCIE EI 2024年第2期2181-2205,共25页
Since its inception,the Internet has been rapidly evolving.With the advancement of science and technology and the explosive growth of the population,the demand for the Internet has been on the rise.Many applications i... Since its inception,the Internet has been rapidly evolving.With the advancement of science and technology and the explosive growth of the population,the demand for the Internet has been on the rise.Many applications in education,healthcare,entertainment,science,and more are being increasingly deployed based on the internet.Concurrently,malicious threats on the internet are on the rise as well.Distributed Denial of Service(DDoS)attacks are among the most common and dangerous threats on the internet today.The scale and complexity of DDoS attacks are constantly growing.Intrusion Detection Systems(IDS)have been deployed and have demonstrated their effectiveness in defense against those threats.In addition,the research of Machine Learning(ML)and Deep Learning(DL)in IDS has gained effective results and significant attention.However,one of the challenges when applying ML and DL techniques in intrusion detection is the identification of unknown attacks.These attacks,which are not encountered during the system’s training,can lead to misclassification with significant errors.In this research,we focused on addressing the issue of Unknown Attack Detection,combining two methods:Spatial Location Constraint Prototype Loss(SLCPL)and Fuzzy C-Means(FCM).With the proposed method,we achieved promising results compared to traditional methods.The proposed method demonstrates a very high accuracy of up to 99.8%with a low false positive rate for known attacks on the Intrusion Detection Evaluation Dataset(CICIDS2017)dataset.Particularly,the accuracy is also very high,reaching 99.7%,and the precision goes up to 99.9%for unknown DDoS attacks on the DDoS Evaluation Dataset(CICDDoS2019)dataset.The success of the proposed method is due to the combination of SLCPL,an advanced Open-Set Recognition(OSR)technique,and FCM,a traditional yet highly applicable clustering technique.This has yielded a novel method in the field of unknown attack detection.This further expands the trend of applying DL and ML techniques in the development of intrusion detection systems and cybersecurity.Finally,implementing the proposed method in real-world systems can enhance the security capabilities against increasingly complex threats on computer networks. 展开更多
关键词 CYBERSECURITY DDoS unknown attack detection machine learning deep learning incremental learning convolutional neural networks(CNN) open-set recognition(OSR) spatial location constraint prototype loss fuzzy c-means CICIDS2017 CICDDoS2019
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Fuzzy C-Means Algorithm Based on Density Canopy and Manifold Learning
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作者 Jili Chen Hailan Wang Xiaolan Xie 《Computer Systems Science & Engineering》 2024年第3期645-663,共19页
Fuzzy C-Means(FCM)is an effective and widely used clustering algorithm,but there are still some problems.considering the number of clusters must be determined manually,the local optimal solutions is easily influenced ... Fuzzy C-Means(FCM)is an effective and widely used clustering algorithm,but there are still some problems.considering the number of clusters must be determined manually,the local optimal solutions is easily influenced by the random selection of initial cluster centers,and the performance of Euclid distance in complex high-dimensional data is poor.To solve the above problems,the improved FCM clustering algorithm based on density Canopy and Manifold learning(DM-FCM)is proposed.First,a density Canopy algorithm based on improved local density is proposed to automatically deter-mine the number of clusters and initial cluster centers,which improves the self-adaptability and stability of the algorithm.Then,considering that high-dimensional data often present a nonlinear structure,the manifold learning method is applied to construct a manifold spatial structure,which preserves the global geometric properties of complex high-dimensional data and improves the clustering effect of the algorithm on complex high-dimensional datasets.Fowlkes-Mallows Index(FMI),the weighted average of homogeneity and completeness(V-measure),Adjusted Mutual Information(AMI),and Adjusted Rand Index(ARI)are used as performance measures of clustering algorithms.The experimental results show that the manifold learning method is the superior distance measure,and the algorithm improves the clustering accuracy and performs superiorly in the clustering of low-dimensional and complex high-dimensional data. 展开更多
关键词 fuzzy c-means(FCM) cluster center density canopy ISOMAP clustering
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Optical Fibre Communication Feature Analysis and Small Sample Fault Diagnosis Based on VMD-FE and Fuzzy Clustering
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作者 Xiangqun Li Jiawen Liang +4 位作者 Jinyu Zhu Shengping Shi Fangyu Ding Jianpeng Sun Bo Liu 《Energy Engineering》 EI 2024年第1期203-219,共17页
To solve the problems of a few optical fibre line fault samples and the inefficiency of manual communication optical fibre fault diagnosis,this paper proposes a communication optical fibre fault diagnosis model based ... To solve the problems of a few optical fibre line fault samples and the inefficiency of manual communication optical fibre fault diagnosis,this paper proposes a communication optical fibre fault diagnosis model based on variational modal decomposition(VMD),fuzzy entropy(FE)and fuzzy clustering(FC).Firstly,based on the OTDR curve data collected in the field,VMD is used to extract the different modal components(IMF)of the original signal and calculate the fuzzy entropy(FE)values of different components to characterize the subtle differences between them.The fuzzy entropy of each curve is used as the feature vector,which in turn constructs the communication optical fibre feature vector matrix,and the fuzzy clustering algorithm is used to achieve fault diagnosis of faulty optical fibre.The VMD-FE combination can extract subtle differences in features,and the fuzzy clustering algorithm does not require sample training.The experimental results show that the model in this paper has high accuracy and is relevant to the maintenance of communication optical fibre when compared with existing feature extraction models and traditional machine learning models. 展开更多
关键词 Optical fibre fault diagnosis OTDR curve variational mode decomposition fuzzy entropy fuzzy clustering
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Effective data transmission through energy-efficient clustering and Fuzzy-Based IDS routing approach in WSNs
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作者 Saziya TABBASSUM Rajesh Kumar PATHAK 《虚拟现实与智能硬件(中英文)》 EI 2024年第1期1-16,共16页
Wireless sensor networks(WSN)gather information and sense information samples in a certain region and communicate these readings to a base station(BS).Energy efficiency is considered a major design issue in the WSNs,a... Wireless sensor networks(WSN)gather information and sense information samples in a certain region and communicate these readings to a base station(BS).Energy efficiency is considered a major design issue in the WSNs,and can be addressed using clustering and routing techniques.Information is sent from the source to the BS via routing procedures.However,these routing protocols must ensure that packets are delivered securely,guaranteeing that neither adversaries nor unauthentic individuals have access to the sent information.Secure data transfer is intended to protect the data from illegal access,damage,or disruption.Thus,in the proposed model,secure data transmission is developed in an energy-effective manner.A low-energy adaptive clustering hierarchy(LEACH)is developed to efficiently transfer the data.For the intrusion detection systems(IDS),Fuzzy logic and artificial neural networks(ANNs)are proposed.Initially,the nodes were randomly placed in the network and initialized to gather information.To ensure fair energy dissipation between the nodes,LEACH randomly chooses cluster heads(CHs)and allocates this role to the various nodes based on a round-robin management mechanism.The intrusion-detection procedure was then utilized to determine whether intruders were present in the network.Within the WSN,a Fuzzy interference rule was utilized to distinguish the malicious nodes from legal nodes.Subsequently,an ANN was employed to distinguish the harmful nodes from suspicious nodes.The effectiveness of the proposed approach was validated using metrics that attained 97%accuracy,97%specificity,and 97%sensitivity of 95%.Thus,it was proved that the LEACH and Fuzzy-based IDS approaches are the best choices for securing data transmission in an energy-efficient manner. 展开更多
关键词 Low energy adaptive clustering hierarchy(LEACH) Intrusion detection system(IDS) Wireless sensor network(WSN) fuzzy logic and artificial neural network(ANN)
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Fuzzy c-means text clustering based on topic concept sub-space 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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ALLIED FUZZY c-MEANS CLUSTERING MODEL 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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基于改进Fuzzy ART的自适应雷达信号分选
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作者 马志峰 张越 +1 位作者 董健 傅雄军 《北京理工大学学报》 EI CAS CSCD 北大核心 2024年第9期990-996,共7页
侦察接收机对获取的辐射源波形去交织以分离不同信号,称为信号分选,是电磁频谱战系统的核心技术.复杂电磁环境下脉冲流密度大、时域波形及诸域特征严重交叠,导致多数基于无监督模型的信号分选方法难以胜任.提出一种可自适应调整警戒阈... 侦察接收机对获取的辐射源波形去交织以分离不同信号,称为信号分选,是电磁频谱战系统的核心技术.复杂电磁环境下脉冲流密度大、时域波形及诸域特征严重交叠,导致多数基于无监督模型的信号分选方法难以胜任.提出一种可自适应调整警戒阈值的模糊自适应共振理论(AVT fuzzy ART)聚类算法,基于对属性差异敏感的曼哈顿距离自适应调整警戒阈值,依据在线累积数据得出的辐射源瞬态聚类概率对警戒阈值动态加权.仿真结果表明,该方法能在无历史先验信息的条件下胜任多类别辐射源信号去交错. 展开更多
关键词 电磁频谱战 雷达信号分选 模糊自适应共振 聚类
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A New Method of Wind Turbine Bearing Fault Diagnosis Based on Multi-Masking Empirical Mode Decomposition and Fuzzy C-Means Clustering 被引量:11
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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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Intuitionistic fuzzy C-means clustering algorithms 被引量:20
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作者 Zeshui Xu Junjie Wu 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 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 被引量:4
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作者 周鲜成 申群太 刘利枚 《Journal of Central South University of Technology》 EI 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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Power interconnected system clustering with advanced fuzzy C-mean algorithm 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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Watershed classification by remote sensing indices: A fuzzy c-means clustering approach 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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Fuzzy c-means clustering based on spatial neighborhood information for image segmentation 被引量:15
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作者 Yanling Li Yi Shen 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2010年第2期323-328,共6页
Fuzzy c-means (FCM) algorithm is one of the most popular methods for image segmentation. However, the standard FCM algorithm is sensitive to noise because of not taking into account the spatial information in the im... Fuzzy c-means (FCM) algorithm is one of the most popular methods for image segmentation. However, the standard FCM algorithm is sensitive to noise because of not taking into account the spatial information in the image. An improved FCM algorithm is proposed to improve the antinoise performance of FCM algorithm. The new algorithm is formulated by incorporating the spatial neighborhood information into the membership function for clustering. The distribution statistics of the neighborhood pixels and the prior probability are used to form a new membership func- tion. It is not only effective to remove the noise spots but also can reduce the misclassified pixels. Experimental results indicate that the proposed algorithm is more accurate and robust to noise than the standard FCM algorithm. 展开更多
关键词 image segmentation fuzzy c-means spatial informa- tion. robust.
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Residual-driven Fuzzy C-Means Clustering for Image Segmentation 被引量:9
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作者 Cong Wang Witold Pedrycz +1 位作者 ZhiWu Li MengChu Zhou 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2021年第4期876-889,共14页
In this paper,we elaborate on residual-driven Fuzzy C-Means(FCM)for image segmentation,which is the first approach that realizes accurate residual(noise/outliers)estimation and enables noise-free image to participate ... In this paper,we elaborate on residual-driven Fuzzy C-Means(FCM)for image segmentation,which is the first approach that realizes accurate residual(noise/outliers)estimation and enables noise-free image to participate in clustering.We propose a residual-driven FCM framework by integrating into FCM a residual-related regularization term derived from the distribution characteristic of different types of noise.Built on this framework,a weighted?2-norm regularization term is presented by weighting mixed noise distribution,thus resulting in a universal residual-driven FCM algorithm in presence of mixed or unknown noise.Besides,with the constraint of spatial information,the residual estimation becomes more reliable than that only considering an observed image itself.Supporting experiments on synthetic,medical,and real-world images are conducted.The results demonstrate the superior effectiveness and efficiency of the proposed algorithm over its peers. 展开更多
关键词 fuzzy c-means image segmentation mixed or unknown noise residual-driven weighted regularization
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A Fixed Suppressed Rate Selection Method for Suppressed Fuzzy C-Means Clustering Algorithm 被引量:2
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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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Picture-Neutrosophic Trusted Safe Semi-Supervised Fuzzy Clustering for Noisy Data
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作者 Pham Huy Thong Florentin Smarandache +5 位作者 Phung The Huan Tran Manh Tuan Tran Thi Ngan Vu Duc Thai Nguyen Long Giang Le Hoang Son 《Computer Systems Science & Engineering》 SCIE EI 2023年第8期1981-1997,共17页
Clustering is a crucial method for deciphering data structure and producing new information.Due to its significance in revealing fundamental connections between the human brain and events,it is essential to utilize cl... Clustering is a crucial method for deciphering data structure and producing new information.Due to its significance in revealing fundamental connections between the human brain and events,it is essential to utilize clustering for cognitive research.Dealing with noisy data caused by inaccurate synthesis from several sources or misleading data production processes is one of the most intriguing clustering difficulties.Noisy data can lead to incorrect object recognition and inference.This research aims to innovate a novel clustering approach,named Picture-Neutrosophic Trusted Safe Semi-Supervised Fuzzy Clustering(PNTS3FCM),to solve the clustering problem with noisy data using neutral and refusal degrees in the definition of Picture Fuzzy Set(PFS)and Neutrosophic Set(NS).Our contribution is to propose a new optimization model with four essential components:clustering,outlier removal,safe semi-supervised fuzzy clustering and partitioning with labeled and unlabeled data.The effectiveness and flexibility of the proposed technique are estimated and compared with the state-of-art methods,standard Picture fuzzy clustering(FC-PFS)and Confidence-weighted safe semi-supervised clustering(CS3FCM)on benchmark UCI datasets.The experimental results show that our method is better at least 10/15 datasets than the compared methods in terms of clustering quality and computational time. 展开更多
关键词 Safe semi-supervised fuzzy clustering picture fuzzy set neutrosophic set data partition with noises fuzzy clustering
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