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
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
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
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
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
A fault diagnosis model is proposed based on fuzzy support vector machine (FSVM) combined with fuzzy clustering (FC).Considering the relationship between the sample point and non-self class,FC algorithm is applied to ...A fault diagnosis model is proposed based on fuzzy support vector machine (FSVM) combined with fuzzy clustering (FC).Considering the relationship between the sample point and non-self class,FC algorithm is applied to generate fuzzy memberships.In the algorithm,sample weights based on a distribution density function of data point and genetic algorithm (GA) are introduced to enhance the performance of FC.Then a multi-class FSVM with radial basis function kernel is established according to directed acyclic graph algorithm,the penalty factor and kernel parameter of which are optimized by GA.Finally,the model is executed for multi-class fault diagnosis of rolling element bearings.The results show that the presented model achieves high performances both in identifying fault types and fault degrees.The performance comparisons of the presented model with SVM and distance-based FSVM for noisy case demonstrate the capacity of dealing with noise and generalization.展开更多
Fuzzy c-means(FCM) clustering algorithm is sensitive to noise points and outlier data, and the possibilistic fuzzy c-means(PFCM) clustering algorithm overcomes the problem well, but PFCM clustering algorithm has some ...Fuzzy c-means(FCM) clustering algorithm is sensitive to noise points and outlier data, and the possibilistic fuzzy c-means(PFCM) clustering algorithm overcomes the problem well, but PFCM clustering algorithm has some problems: it is still sensitive to initial clustering centers and the clustering results are not good when the tested datasets with noise are very unequal. An improved kernel possibilistic fuzzy c-means algorithm based on invasive weed optimization(IWO-KPFCM) is proposed in this paper. This algorithm first uses invasive weed optimization(IWO) algorithm to seek the optimal solution as the initial clustering centers, and introduces kernel method to make the input data from the sample space map into the high-dimensional feature space. Then, the sample variance is introduced in the objection function to measure the compact degree of data. Finally, the improved algorithm is used to cluster data. The simulation results of the University of California-Irvine(UCI) data sets and artificial data sets show that the proposed algorithm has stronger ability to resist noise, higher cluster accuracy and faster convergence speed than the PFCM algorithm.展开更多
Recently,the fundamental problem with Hybrid Mobile Ad-hoc Net-works(H-MANETs)is tofind a suitable and secure way of balancing the load through Internet gateways.Moreover,the selection of the gateway and overload of th...Recently,the fundamental problem with Hybrid Mobile Ad-hoc Net-works(H-MANETs)is tofind a suitable and secure way of balancing the load through Internet gateways.Moreover,the selection of the gateway and overload of the network results in packet loss and Delay(DL).For optimal performance,it is important to load balance between different gateways.As a result,a stable load balancing procedure is implemented,which selects gateways based on Fuzzy Logic(FL)and increases the efficiency of the network.In this case,since gate-ways are selected based on the number of nodes,the Energy Consumption(EC)was high.This paper presents a novel Node Quality-based Clustering Algo-rithm(NQCA)based on Fuzzy-Genetic for Cluster Head and Gateway Selection(FGCHGS).This algorithm combines NQCA with the Improved Weighted Clus-tering Algorithm(IWCA).The NQCA algorithm divides the network into clusters based upon node priority,transmission range,and neighbourfidelity.In addition,the simulation results tend to evaluate the performance effectiveness of the FFFCHGS algorithm in terms of EC,packet loss rate(PLR),etc.展开更多
The premise and basis of load modeling are substation load composition inquiries and cluster analyses.However,the traditional kernel fuzzy C-means(KFCM)algorithm is limited by artificial clustering number selection an...The premise and basis of load modeling are substation load composition inquiries and cluster analyses.However,the traditional kernel fuzzy C-means(KFCM)algorithm is limited by artificial clustering number selection and its convergence to local optimal solutions.To overcome these limitations,an improved KFCM algorithm with adaptive optimal clustering number selection is proposed in this paper.This algorithm optimizes the KFCM algorithm by combining the powerful global search ability of genetic algorithm and the robust local search ability of simulated annealing algorithm.The improved KFCM algorithm adaptively determines the ideal number of clusters using the clustering evaluation index ratio.Compared with the traditional KFCM algorithm,the enhanced KFCM algorithm has robust clustering and comprehensive abilities,enabling the efficient convergence to the global optimal solution.展开更多
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
Fuzzy C-means clustering algorithm is a classical non-supervised classification method.For image classification, fuzzy C-means clustering algorithm makes decisions on a pixel-by-pixel basis and does not take advantage...Fuzzy C-means clustering algorithm is a classical non-supervised classification method.For image classification, fuzzy C-means clustering algorithm makes decisions on a pixel-by-pixel basis and does not take advantage of spatial information, regardless of the pixels' correlation. In this letter, a novel fuzzy C-means clustering algorithm is introduced, which is based on image's neighborhood system. During classification procedure, the novel algorithm regards all pixels'fuzzy membership as a random field. The neighboring pixels' fuzzy membership information is used for the algorithm's iteration procedure. As a result, the algorithm gives a more smooth classification result and cuts down the computation time.展开更多
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
Wind farm power prediction is proposed based on adaptive feature weight entropy fuzzy clustering algorithm.According to the fuzzy clustering method,a large number of historical data of a wind farm in Inner Mongolia ar...Wind farm power prediction is proposed based on adaptive feature weight entropy fuzzy clustering algorithm.According to the fuzzy clustering method,a large number of historical data of a wind farm in Inner Mongolia are analyzed and classified.Model of adaptive entropy weight for clustering is built.Wind power prediction model based on adaptive entropy fuzzy clustering feature weights is built.Simulation results show that the proposed method could distinguish the abnormal data and forecast more accurately and compute fastly.展开更多
Feature extraction of range images provided by ranging sensor is a key issue of pattern recognition. To automatically extract the environmental feature sensed by a 2D ranging sensor laser scanner, an improved method b...Feature extraction of range images provided by ranging sensor is a key issue of pattern recognition. To automatically extract the environmental feature sensed by a 2D ranging sensor laser scanner, an improved method based on genetic clustering VGA-clustering is presented. By integrating the spatial neighbouring information of range data into fuzzy clustering algorithm, a weighted fuzzy clustering algorithm （WFCA） instead of standard clustering algorithm is introduced to realize feature extraction of laser scanner. Aimed at the unknown clustering number in advance, several validation index functions are used to estimate the validity of different clustering algorithms and one validation index is selected as the fitness function of genetic algorithm so as to determine the accurate clustering number automatically. At the same time, an improved genetic algorithm IVGA on the basis of VGA is proposed to solve the local optimum of clustering algorithm, which is implemented by increasing the population diversity and improving the genetic operators of elitist rule to enhance the local search capacity and to quicken the convergence speed. By the comparison with other algorithms, the effectiveness of the algorithm introduced is demonstrated.展开更多
In this letter, a new method is proposed for unsupervised classification of terrain types and man-made objects using POLarimetric Synthetic Aperture Radar (POLSAR) data. This technique is a combi-nation of the usage o...In this letter, a new method is proposed for unsupervised classification of terrain types and man-made objects using POLarimetric Synthetic Aperture Radar (POLSAR) data. This technique is a combi-nation of the usage of polarimetric information of SAR images and the unsupervised classification method based on fuzzy set theory. Image quantization and image enhancement are used to preprocess the POLSAR data. Then the polarimetric information and Fuzzy C-Means (FCM) clustering algorithm are used to classify the preprocessed images. The advantages of this algorithm are the automated classification, its high classifica-tion accuracy, fast convergence and high stability. The effectiveness of this algorithm is demonstrated by ex-periments using SIR-C/X-SAR (Spaceborne Imaging Radar-C/X-band Synthetic Aperture Radar) data.展开更多
Data analysis and automatic processing is often interpreted as knowledge acquisition. In many cases it is necessary to somehow classify data or find regularities in them. Results obtained in the search of regularities...Data analysis and automatic processing is often interpreted as knowledge acquisition. In many cases it is necessary to somehow classify data or find regularities in them. Results obtained in the search of regularities in intelligent data analyzing applications are mostly represented with the help of IF-THEN rules. With the help of these rules the following tasks are solved： prediction, classification, pattern recognition and others. Using different approaches---clustering algorithms, neural network methods, fuzzy rule processing methods--we can extract rules that in an understandable language characterize the data. This allows interpreting the data, finding relationships in the data and extracting new rules that characterize them. Knowledge acquisition in this paper is defined as the process of extracting knowledge from numerical data in the form of rules. Extraction of rules in this context is based on clustering methods K-means and fuzzy C-means. With the assistance of K-means, clustering algorithm rules are derived from trained neural networks. Fuzzy C-means is used in fuzzy rule based design method. Rule extraction methodology is demonstrated in the Fisher＇s Iris flower data set samples. The effectiveness of the extracted rules is evaluated. Clustering and rule extraction methodology can be widely used in evaluating and analyzing various economic and financial processes.展开更多
In this paper, the authors present three different algorithms for data clustering. These are Self-Organizing Map （SOM）, Neural Gas （NG） and Fuzzy C-Means （FCM） algorithms. SOM and NG algorithms are based on comp...In this paper, the authors present three different algorithms for data clustering. These are Self-Organizing Map （SOM）, Neural Gas （NG） and Fuzzy C-Means （FCM） algorithms. SOM and NG algorithms are based on competitive leaming. An important property of these algorithms is that they preserve the topological structure of data. This means that data that is close in input distribution is mapped to nearby locations in the network. The FCM algorithm is an algorithm based on soft clustering which means that the different clusters are not necessarily distinct, but may overlap. This clustering method may be very useful in many biological problems, for instance in genetics, where a gene may belong to different clusters. The different algorithms are compared in terms of their visualization of the clustering of proteomic data.展开更多
In the last decade, the MRI (Magnetic Resonance Imaging) image segmentation has become one of the most active research fields in the medical imaging domain. Because of the fuzzy nature of the MRI images, many research...In the last decade, the MRI (Magnetic Resonance Imaging) image segmentation has become one of the most active research fields in the medical imaging domain. Because of the fuzzy nature of the MRI images, many researchers have adopted the fuzzy clustering approach to segment them. In this work, a fast and robust multi-agent system (MAS) for MRI segmentation of the brain is proposed. This system gets its robustness from a robust c-means algorithm (RFCM) and obtains its fastness from the beneficial properties of agents, such as autonomy, social ability and reactivity. To show the efficiency of the proposed method, we test it on a normal brain brought from the BrainWeb Simulated Brain Database. The experimental results are valuable in both robustness to noise and running times standpoints.展开更多
提出CF-WFCM算法,该算法分为属性权重学习算法和聚类算法两部分.属性权重学习算法,从数据自身的相似性出发,通过梯度递减算法极小化属性评价函数CFuzziness(w),为每个属性赋予一个权重.将属性权重应用于Fuzzy C Mean聚类算法,得到CF-WFC...提出CF-WFCM算法,该算法分为属性权重学习算法和聚类算法两部分.属性权重学习算法,从数据自身的相似性出发,通过梯度递减算法极小化属性评价函数CFuzziness(w),为每个属性赋予一个权重.将属性权重应用于Fuzzy C Mean聚类算法,得到CF-WFCM算法的聚类算法.CF-WFCM算法强化重要属性在聚类过程中的作用,消减冗余属性的作用,从而改善聚类的效果.我们选取了部分UCI数据库进行实验,实验结果证明:CF-WFCM算法的聚类结果优于FCM算法的聚类结果.函数CFuzziness(w)不仅可以评价属性的重要性,而且可以评价属性评价函数的优劣.实验说明了这一问题.最后我们对CF-WFCM算法进行了讨论.展开更多
文摘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.
文摘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.
基金supported by the National Research and Development Project of China (2020YFB2008400).
文摘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.
基金Supported by the National Natural Science Foundation of China(61139002)~~
文摘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.
文摘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.
基金Supported by the joint fund of National Natural Science Foundation of China and Civil Aviation Administration Foundation of China(No.U1233201)
文摘A fault diagnosis model is proposed based on fuzzy support vector machine (FSVM) combined with fuzzy clustering (FC).Considering the relationship between the sample point and non-self class,FC algorithm is applied to generate fuzzy memberships.In the algorithm,sample weights based on a distribution density function of data point and genetic algorithm (GA) are introduced to enhance the performance of FC.Then a multi-class FSVM with radial basis function kernel is established according to directed acyclic graph algorithm,the penalty factor and kernel parameter of which are optimized by GA.Finally,the model is executed for multi-class fault diagnosis of rolling element bearings.The results show that the presented model achieves high performances both in identifying fault types and fault degrees.The performance comparisons of the presented model with SVM and distance-based FSVM for noisy case demonstrate the capacity of dealing with noise and generalization.
文摘Fuzzy c-means(FCM) clustering algorithm is sensitive to noise points and outlier data, and the possibilistic fuzzy c-means(PFCM) clustering algorithm overcomes the problem well, but PFCM clustering algorithm has some problems: it is still sensitive to initial clustering centers and the clustering results are not good when the tested datasets with noise are very unequal. An improved kernel possibilistic fuzzy c-means algorithm based on invasive weed optimization(IWO-KPFCM) is proposed in this paper. This algorithm first uses invasive weed optimization(IWO) algorithm to seek the optimal solution as the initial clustering centers, and introduces kernel method to make the input data from the sample space map into the high-dimensional feature space. Then, the sample variance is introduced in the objection function to measure the compact degree of data. Finally, the improved algorithm is used to cluster data. The simulation results of the University of California-Irvine(UCI) data sets and artificial data sets show that the proposed algorithm has stronger ability to resist noise, higher cluster accuracy and faster convergence speed than the PFCM algorithm.
文摘Recently,the fundamental problem with Hybrid Mobile Ad-hoc Net-works(H-MANETs)is tofind a suitable and secure way of balancing the load through Internet gateways.Moreover,the selection of the gateway and overload of the network results in packet loss and Delay(DL).For optimal performance,it is important to load balance between different gateways.As a result,a stable load balancing procedure is implemented,which selects gateways based on Fuzzy Logic(FL)and increases the efficiency of the network.In this case,since gate-ways are selected based on the number of nodes,the Energy Consumption(EC)was high.This paper presents a novel Node Quality-based Clustering Algo-rithm(NQCA)based on Fuzzy-Genetic for Cluster Head and Gateway Selection(FGCHGS).This algorithm combines NQCA with the Improved Weighted Clus-tering Algorithm(IWCA).The NQCA algorithm divides the network into clusters based upon node priority,transmission range,and neighbourfidelity.In addition,the simulation results tend to evaluate the performance effectiveness of the FFFCHGS algorithm in terms of EC,packet loss rate(PLR),etc.
基金supported by the Planning Special Project of Guangdong Power Grid Co.,Ltd.:“Study on load modeling based on total measurement and discrimination method suitable for system characteristic analysis and calculation during the implementation of target grid in Guangdong power grid”(0319002022030203JF00023).
文摘The premise and basis of load modeling are substation load composition inquiries and cluster analyses.However,the traditional kernel fuzzy C-means(KFCM)algorithm is limited by artificial clustering number selection and its convergence to local optimal solutions.To overcome these limitations,an improved KFCM algorithm with adaptive optimal clustering number selection is proposed in this paper.This algorithm optimizes the KFCM algorithm by combining the powerful global search ability of genetic algorithm and the robust local search ability of simulated annealing algorithm.The improved KFCM algorithm adaptively determines the ideal number of clusters using the clustering evaluation index ratio.Compared with the traditional KFCM algorithm,the enhanced KFCM algorithm has robust clustering and comprehensive abilities,enabling the efficient convergence to the global optimal solution.
基金the China Agriculture Research System(No.CARS-49)Jiangsu College of Humanities and Social Sciences Outside Campus Research Base & Chinese Development of Strategic Research Base for Internet of Things
文摘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.
文摘Fuzzy C-means clustering algorithm is a classical non-supervised classification method.For image classification, fuzzy C-means clustering algorithm makes decisions on a pixel-by-pixel basis and does not take advantage of spatial information, regardless of the pixels' correlation. In this letter, a novel fuzzy C-means clustering algorithm is introduced, which is based on image's neighborhood system. During classification procedure, the novel algorithm regards all pixels'fuzzy membership as a random field. The neighboring pixels' fuzzy membership information is used for the algorithm's iteration procedure. As a result, the algorithm gives a more smooth classification result and cuts down the computation time.
文摘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%.
基金supported by the Natural Science Foundation of China under contact(61233007)
文摘Wind farm power prediction is proposed based on adaptive feature weight entropy fuzzy clustering algorithm.According to the fuzzy clustering method,a large number of historical data of a wind farm in Inner Mongolia are analyzed and classified.Model of adaptive entropy weight for clustering is built.Wind power prediction model based on adaptive entropy fuzzy clustering feature weights is built.Simulation results show that the proposed method could distinguish the abnormal data and forecast more accurately and compute fastly.
基金the National Natural Science Foundation of China (60234030)the Natural Science Foundationof He’nan Educational Committee of China (2007520019, 2008B520015)Doctoral Foundation of Henan Polytechnic Universityof China (B050901, B2008-61)
文摘Feature extraction of range images provided by ranging sensor is a key issue of pattern recognition. To automatically extract the environmental feature sensed by a 2D ranging sensor laser scanner, an improved method based on genetic clustering VGA-clustering is presented. By integrating the spatial neighbouring information of range data into fuzzy clustering algorithm, a weighted fuzzy clustering algorithm （WFCA） instead of standard clustering algorithm is introduced to realize feature extraction of laser scanner. Aimed at the unknown clustering number in advance, several validation index functions are used to estimate the validity of different clustering algorithms and one validation index is selected as the fitness function of genetic algorithm so as to determine the accurate clustering number automatically. At the same time, an improved genetic algorithm IVGA on the basis of VGA is proposed to solve the local optimum of clustering algorithm, which is implemented by increasing the population diversity and improving the genetic operators of elitist rule to enhance the local search capacity and to quicken the convergence speed. By the comparison with other algorithms, the effectiveness of the algorithm introduced is demonstrated.
基金Supported by the University Doctorate Special Research Fund (No. 20030614001) and the Youth Scholarship Leader Fund of Univ. of Electro. Sci. and Tech. of China.
文摘In this letter, a new method is proposed for unsupervised classification of terrain types and man-made objects using POLarimetric Synthetic Aperture Radar (POLSAR) data. This technique is a combi-nation of the usage of polarimetric information of SAR images and the unsupervised classification method based on fuzzy set theory. Image quantization and image enhancement are used to preprocess the POLSAR data. Then the polarimetric information and Fuzzy C-Means (FCM) clustering algorithm are used to classify the preprocessed images. The advantages of this algorithm are the automated classification, its high classifica-tion accuracy, fast convergence and high stability. The effectiveness of this algorithm is demonstrated by ex-periments using SIR-C/X-SAR (Spaceborne Imaging Radar-C/X-band Synthetic Aperture Radar) data.
文摘Data analysis and automatic processing is often interpreted as knowledge acquisition. In many cases it is necessary to somehow classify data or find regularities in them. Results obtained in the search of regularities in intelligent data analyzing applications are mostly represented with the help of IF-THEN rules. With the help of these rules the following tasks are solved： prediction, classification, pattern recognition and others. Using different approaches---clustering algorithms, neural network methods, fuzzy rule processing methods--we can extract rules that in an understandable language characterize the data. This allows interpreting the data, finding relationships in the data and extracting new rules that characterize them. Knowledge acquisition in this paper is defined as the process of extracting knowledge from numerical data in the form of rules. Extraction of rules in this context is based on clustering methods K-means and fuzzy C-means. With the assistance of K-means, clustering algorithm rules are derived from trained neural networks. Fuzzy C-means is used in fuzzy rule based design method. Rule extraction methodology is demonstrated in the Fisher＇s Iris flower data set samples. The effectiveness of the extracted rules is evaluated. Clustering and rule extraction methodology can be widely used in evaluating and analyzing various economic and financial processes.
文摘In this paper, the authors present three different algorithms for data clustering. These are Self-Organizing Map （SOM）, Neural Gas （NG） and Fuzzy C-Means （FCM） algorithms. SOM and NG algorithms are based on competitive leaming. An important property of these algorithms is that they preserve the topological structure of data. This means that data that is close in input distribution is mapped to nearby locations in the network. The FCM algorithm is an algorithm based on soft clustering which means that the different clusters are not necessarily distinct, but may overlap. This clustering method may be very useful in many biological problems, for instance in genetics, where a gene may belong to different clusters. The different algorithms are compared in terms of their visualization of the clustering of proteomic data.
文摘In the last decade, the MRI (Magnetic Resonance Imaging) image segmentation has become one of the most active research fields in the medical imaging domain. Because of the fuzzy nature of the MRI images, many researchers have adopted the fuzzy clustering approach to segment them. In this work, a fast and robust multi-agent system (MAS) for MRI segmentation of the brain is proposed. This system gets its robustness from a robust c-means algorithm (RFCM) and obtains its fastness from the beneficial properties of agents, such as autonomy, social ability and reactivity. To show the efficiency of the proposed method, we test it on a normal brain brought from the BrainWeb Simulated Brain Database. The experimental results are valuable in both robustness to noise and running times standpoints.
文摘提出CF-WFCM算法,该算法分为属性权重学习算法和聚类算法两部分.属性权重学习算法,从数据自身的相似性出发,通过梯度递减算法极小化属性评价函数CFuzziness(w),为每个属性赋予一个权重.将属性权重应用于Fuzzy C Mean聚类算法,得到CF-WFCM算法的聚类算法.CF-WFCM算法强化重要属性在聚类过程中的作用,消减冗余属性的作用,从而改善聚类的效果.我们选取了部分UCI数据库进行实验,实验结果证明:CF-WFCM算法的聚类结果优于FCM算法的聚类结果.函数CFuzziness(w)不仅可以评价属性的重要性,而且可以评价属性评价函数的优劣.实验说明了这一问题.最后我们对CF-WFCM算法进行了讨论.
