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
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
A fast and effective fuzzy clustering algorithm is proposed. The algorithm splits an image into n × n blocks, and uses block variance to judge whether the block region is homogeneous. Mean and center pixel of eac...A fast and effective fuzzy clustering algorithm is proposed. The algorithm splits an image into n × n blocks, and uses block variance to judge whether the block region is homogeneous. Mean and center pixel of each homogeneous block are extracted for feature. Each inhomogeneous block is split into separate pixels and the mean of neighboring pixels within a window around each pixel and pixel value are extracted for feature. Then cluster of homogeneous blocks and cluster of separate pixels from inhomogeneous blocks are carried out respectively according to different membership functions. In fuzzy clustering stage, the center pixel and center number of the initial clustering are calculated based on histogram by using mean feature. Then different membership functions according to comparative result of block variance are computed. Finally, modified fuzzy c-means with spatial information to complete image segmentation axe used. Experimental results show that the proposed method can achieve better segmental results and has shorter executive time than many well-known methods.展开更多
A novel radio-map establishment based on fuzzy clustering for hybrid K-Nearest Neighbor (KNN) and Artifi cial Neural Network (ANN) position algorithm in WLAN indoor environment is proposed. First of all, the Principal...A novel radio-map establishment based on fuzzy clustering for hybrid K-Nearest Neighbor (KNN) and Artifi cial Neural Network (ANN) position algorithm in WLAN indoor environment is proposed. First of all, the Principal Component Analysis (PCA) is utilized for the purpose of simplifying input dimensions of position estimation algorithm and saving storage cost for the establishment of radio-map. Then, reference points (RPs) calibrated in the off-line phase are divided into separate clusters by Fuzzy C-means clustering (FCM), and membership degrees (MDs) for different clusters are also allocated to each RPs. However, the singular RPs cased by the multi-path effect signifi cantly decreases the clustering performance. Therefore, a novel radio-map establishment method is presented based on the modifi cation of signal samples recorded at singular RPs by surface fitting. In the on-line phase, the region which the mobile terminal (MT) belongs to is estimated according to the MDs firstly. Then, in estimated small dimensional regions, MT's coordinates are calculated byKNN positioning method for efficiency purpose. However, for the regions including singular RPs, ANN method is utilized because ofits great pattern matching ability. Furthermore, compared with other typical indoor positioning methods, feasibility and effectiveness of this hybrid KNN/ANN method are also verified by the experimental results in static and tracking situations.展开更多
For the charging station construction of electric vehicle,location selecting is a key issue.There are two problems in location selection of the electric vehicle charging station.One is determining the location of char...For the charging station construction of electric vehicle,location selecting is a key issue.There are two problems in location selection of the electric vehicle charging station.One is determining the location of charging station;the other is evaluating the location of charging station.To determine the charging station location,an spatial clustering algorithm is proposed and programmed.The example simulation shows the effectiveness of the spatial clustering algorithm.To evaluate the charging station location,a multi-hierarchical fuzzy method is proposed.Based on the location factors of electric vehicle charging station,the hierarchical evaluation structure of electric vehicle charging station location is constructed,including three levels,4first-class factors and 14second-class factors.The fuzzy multi-hierarchical evaluation model and algorithm are built.The analysis results show that the multi-hierarchical fuzzy method can reasonably complete the electric vehicle charging station location evaluation.展开更多
Aimed at the issue that traditional clustering methods are not appropriate to high-dimensional data, a cuckoo search fuzzy-weighting algorithm for subspace clustering is presented on the basis of the exited soft subsp...Aimed at the issue that traditional clustering methods are not appropriate to high-dimensional data, a cuckoo search fuzzy-weighting algorithm for subspace clustering is presented on the basis of the exited soft subspace clustering algorithm. In the proposed algorithm, a novel objective function is firstly designed by considering the fuzzy weighting within-cluster compactness and the between-cluster separation, and loosening the constraints of dimension weight matrix. Then gradual membership and improved Cuckoo search, a global search strategy, are introduced to optimize the objective function and search subspace clusters, giving novel learning rules for clustering. At last, the performance of the proposed algorithm on the clustering analysis of various low and high dimensional datasets is experimentally compared with that of several competitive subspace clustering algorithms. Experimental studies demonstrate that the proposed algorithm can obtain better performance than most of the existing soft subspace clustering algorithms.展开更多
Target maneuver recognition is a prerequisite for air combat situation awareness,trajectory prediction,threat assessment and maneuver decision.To get rid of the dependence of the current target maneuver recognition me...Target maneuver recognition is a prerequisite for air combat situation awareness,trajectory prediction,threat assessment and maneuver decision.To get rid of the dependence of the current target maneuver recognition method on empirical criteria and sample data,and automatically and adaptively complete the task of extracting the target maneuver pattern,in this paper,an air combat maneuver pattern extraction based on time series segmentation and clustering analysis is proposed by combining autoencoder,G-G clustering algorithm and the selective ensemble clustering analysis algorithm.Firstly,the autoencoder is used to extract key features of maneuvering trajectory to remove the impacts of redundant variables and reduce the data dimension;Then,taking the time information into account,the segmentation of Maneuver characteristic time series is realized with the improved FSTS-AEGG algorithm,and a large number of maneuver primitives are extracted;Finally,the maneuver primitives are grouped into some categories by using the selective ensemble multiple time series clustering algorithm,which can prove that each class represents a maneuver action.The maneuver pattern extraction method is applied to small scale air combat trajectory and can recognize and correctly partition at least 71.3%of maneuver actions,indicating that the method is effective and satisfies the requirements for engineering accuracy.In addition,this method can provide data support for various target maneuvering recognition methods proposed in the literature,greatly reduce the workload and improve the recognition accuracy.展开更多
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
Image segmentation remains one of the major challenges in image analysis.And soft image segmentation has been widely used due to its good effect.Fuzzy clustering algorithms are very popular in soft segmentation.A new ...Image segmentation remains one of the major challenges in image analysis.And soft image segmentation has been widely used due to its good effect.Fuzzy clustering algorithms are very popular in soft segmentation.A new soft image segmentation method based on center-free fuzzy clustering is proposed.The center-free fuzzy clustering is the modified version of the classical fuzzy C-means ( FCM ) clustering.Different from traditional fuzzy clustering , the center-free fuzzy clustering does not need to calculate the cluster center , so it can be applied to pairwise relational data.In the proposed method , the mean-shift method is chosen for initial segmentation firstly , then the center-free clustering is used to merge regions and the final segmented images are obtained at last.Experimental results show that the proposed method is better than other image segmentation methods based on traditional clustering.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
A novel mercer kernel based fuzzy clustering self-adaptive algorithm is presented. The mercer kernel method is introduced to the fuzzy c-means clustering. It may map implicitly the input data into the high-dimensional...A novel mercer kernel based fuzzy clustering self-adaptive algorithm is presented. The mercer kernel method is introduced to the fuzzy c-means clustering. It may map implicitly the input data into the high-dimensional feature space through the nonlinear transformation. Among other fuzzy c-means and its variants, the number of clusters is first determined. A self-adaptive algorithm is proposed. The number of clusters, which is not given in advance, can be gotten automatically by a validity measure function. Finally, experiments are given to show better performance with the method of kernel based fuzzy c-means self-adaptive algorithm.展开更多
This paper presents a new Section Set Adaptive FCM algorithm.The algorithm solved the shortcomings of local optimality,unsure classification and clustering numbers ascertained previously.And it improved on the archite...This paper presents a new Section Set Adaptive FCM algorithm.The algorithm solved the shortcomings of local optimality,unsure classification and clustering numbers ascertained previously.And it improved on the architecture of FCM al- gorithm,enhanced the analysis for effective clustering.During the clustering processing,it may adjust clustering numbers dy- namically.Finally,it used the method of section set decreasing the time of classification.By experiments,the algorithm can im- prove dependability of clustering and correctness of classification.展开更多
The complex geometry and topology of soil is widely recognised as the key driver in many ecological processes. X-ray computed tomography (CT) provides insight into the internal structure of soil pores automatically an...The complex geometry and topology of soil is widely recognised as the key driver in many ecological processes. X-ray computed tomography (CT) provides insight into the internal structure of soil pores automatically and accurately. Until recently, there have not been methods to identify soil pore structures. This has restricted the development of soil science, particularly regarding pore geometry and spatial distribution. Through the adoption of the fuzzy clustering theory and the establishment of pore identification rules, a novel pore identification method is described to extract pore structures from CT soil images. The robustness of the adaptive fuzzy C-means method (AFCM), the adaptive threshold method, and Image-Pro Plus tools were compared on soil specimens under different conditions, such as frozen, saturated, and dry situations. The results demonstrate that the AFCM method is suitable for identifying pore clusters, especially tiny pores, under various soil conditions. The method would provide an optional technique for the study of soil micromorphology.展开更多
Spontaneous combustion is one of the greatest disasters in coal mines. Early recognition is important because it may be a potential inducement for other coalmine accidents. However, early recognition is difficult beca...Spontaneous combustion is one of the greatest disasters in coal mines. Early recognition is important because it may be a potential inducement for other coalmine accidents. However, early recognition is difficult because of the complexity of different coal mines. Fuzzy clustering has been proposed to incorporate the uncertainty of spontaneous combustion in coal mines and it can give a clear degree of classification of combustion. Because FCM clustering tends to become trapped in local minima, a new approach of fuzzy c-means clustering based on a genetic algorithm is there- fore proposed. Genetic algorithm is capable of locating optimal or near optimal solutions to difficult problems. It can be applied in many fields without first obtaining detailed knowledge about correlation. It is helpful in improving the effec- tiveness of fuzzy clustering in detecting spontaneous combustion. The effectiveness of the method is demonstrated by means of an experiment.展开更多
The S/N of an underwater image is low and has a fuzzy edge.If using traditional methods to process it directly,the result is not satisfying.Though the traditional fuzzy C-means algorithm can sometimes divide the image...The S/N of an underwater image is low and has a fuzzy edge.If using traditional methods to process it directly,the result is not satisfying.Though the traditional fuzzy C-means algorithm can sometimes divide the image into object and background,its time-consuming computation is often an obstacle.The mission of the vision system of an autonomous underwater vehicle (AUV) is to rapidly and exactly deal with the information about the object in a complex environment for the AUV to use the obtained result to execute the next task.So,by using the statistical characteristics of the gray image histogram,a fast and effective fuzzy C-means underwater image segmentation algorithm was presented.With the weighted histogram modifying the fuzzy membership,the above algorithm can not only cut down on a large amount of data processing and storage during the computation process compared with the traditional algorithm,so as to speed up the efficiency of the segmentation,but also improve the quality of underwater image segmentation.Finally,particle swarm optimization (PSO) described by the sine function was introduced to the algorithm mentioned above.It made up for the shortcomings that the FCM algorithm can not get the global optimal solution.Thus,on the one hand,it considers the global impact and achieves the local optimal solution,and on the other hand,further greatly increases the computing speed.Experimental results indicate that the novel algorithm can reach a better segmentation quality and the processing time of each image is reduced.They enhance efficiency and satisfy the requirements of a highly effective,real-time AUV.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
In recent years, microarray technology has been widely applied in biological and clinical studies for simultaneous monitoring of gene expression in thousands of genes. Gene clustering analysis is found useful for disc...In recent years, microarray technology has been widely applied in biological and clinical studies for simultaneous monitoring of gene expression in thousands of genes. Gene clustering analysis is found useful for discovering groups of correlated genes potentially co-regulated or associated to the disease or conditions under investigation. Many clustering methods including k-means, fuzzy c-means, and hierarchical clustering have been widely used in literatures. Yet no comprehensive comparative study has been performed to evaluate the effectiveness of these methods, specially, in yeast saccharomyces cerevisiae. In this paper, these three gene clustering methods are compared. Classification accuracy and CPU time cost are employed for measuring performance of these algorithms. Our results show that hierarchical clustering outperforms k-means and fuzzy c-means clustering. The analysis provides deep insight to the complicated gene clustering problem of expression profile and serves as a practical guideline for routine microarray cluster analysis of gene expression.展开更多
To deal with the nonlinear separable problem, the generalized noise clustering （GNC） algorithm is extended to a kernel generalized noise clustering （KGNC） model. Different from the fuzzy c-means （FCM） model and ...To deal with the nonlinear separable problem, the generalized noise clustering （GNC） algorithm is extended to a kernel generalized noise clustering （KGNC） model. Different from the fuzzy c-means （FCM） model and the GNC model which are based on Euclidean distance, the presented model is based on kernel-induced distance by using kernel method. By kernel method the input data are nonlinearly and implicitly mapped into a high-dimensional feature space, where the nonlinear pattern appears linear and the GNC algorithm is performed. It is unnecessary to calculate in high-dimensional feature space because the kernel function can do it just in input space. The effectiveness of the proposed algorithm is verified by experiments on three data sets. It is concluded that the KGNC algorithm has better clustering accuracy than FCM and GNC in clustering data sets containing noisy data.展开更多
文摘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.
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A fast and effective fuzzy clustering algorithm is proposed. The algorithm splits an image into n × n blocks, and uses block variance to judge whether the block region is homogeneous. Mean and center pixel of each homogeneous block are extracted for feature. Each inhomogeneous block is split into separate pixels and the mean of neighboring pixels within a window around each pixel and pixel value are extracted for feature. Then cluster of homogeneous blocks and cluster of separate pixels from inhomogeneous blocks are carried out respectively according to different membership functions. In fuzzy clustering stage, the center pixel and center number of the initial clustering are calculated based on histogram by using mean feature. Then different membership functions according to comparative result of block variance are computed. Finally, modified fuzzy c-means with spatial information to complete image segmentation axe used. Experimental results show that the proposed method can achieve better segmental results and has shorter executive time than many well-known methods.
基金supported by National High-Tech Research & Development Program of China (Grant No. 2008AA12Z305)
文摘A novel radio-map establishment based on fuzzy clustering for hybrid K-Nearest Neighbor (KNN) and Artifi cial Neural Network (ANN) position algorithm in WLAN indoor environment is proposed. First of all, the Principal Component Analysis (PCA) is utilized for the purpose of simplifying input dimensions of position estimation algorithm and saving storage cost for the establishment of radio-map. Then, reference points (RPs) calibrated in the off-line phase are divided into separate clusters by Fuzzy C-means clustering (FCM), and membership degrees (MDs) for different clusters are also allocated to each RPs. However, the singular RPs cased by the multi-path effect signifi cantly decreases the clustering performance. Therefore, a novel radio-map establishment method is presented based on the modifi cation of signal samples recorded at singular RPs by surface fitting. In the on-line phase, the region which the mobile terminal (MT) belongs to is estimated according to the MDs firstly. Then, in estimated small dimensional regions, MT's coordinates are calculated byKNN positioning method for efficiency purpose. However, for the regions including singular RPs, ANN method is utilized because ofits great pattern matching ability. Furthermore, compared with other typical indoor positioning methods, feasibility and effectiveness of this hybrid KNN/ANN method are also verified by the experimental results in static and tracking situations.
基金supported by the National Natural Science Foundation of China(No.51575047)
文摘For the charging station construction of electric vehicle,location selecting is a key issue.There are two problems in location selection of the electric vehicle charging station.One is determining the location of charging station;the other is evaluating the location of charging station.To determine the charging station location,an spatial clustering algorithm is proposed and programmed.The example simulation shows the effectiveness of the spatial clustering algorithm.To evaluate the charging station location,a multi-hierarchical fuzzy method is proposed.Based on the location factors of electric vehicle charging station,the hierarchical evaluation structure of electric vehicle charging station location is constructed,including three levels,4first-class factors and 14second-class factors.The fuzzy multi-hierarchical evaluation model and algorithm are built.The analysis results show that the multi-hierarchical fuzzy method can reasonably complete the electric vehicle charging station location evaluation.
基金supported in part by the National Natural Science Foundation of China (Nos. 61303074, 61309013)the Programs for Science, National Key Basic Research and Development Program ("973") of China (No. 2012CB315900)Technology Development of Henan province (Nos.12210231003, 13210231002)
文摘Aimed at the issue that traditional clustering methods are not appropriate to high-dimensional data, a cuckoo search fuzzy-weighting algorithm for subspace clustering is presented on the basis of the exited soft subspace clustering algorithm. In the proposed algorithm, a novel objective function is firstly designed by considering the fuzzy weighting within-cluster compactness and the between-cluster separation, and loosening the constraints of dimension weight matrix. Then gradual membership and improved Cuckoo search, a global search strategy, are introduced to optimize the objective function and search subspace clusters, giving novel learning rules for clustering. At last, the performance of the proposed algorithm on the clustering analysis of various low and high dimensional datasets is experimentally compared with that of several competitive subspace clustering algorithms. Experimental studies demonstrate that the proposed algorithm can obtain better performance than most of the existing soft subspace clustering algorithms.
基金supported by the National Natural Science Foundation of China (Project No.72301293)。
文摘Target maneuver recognition is a prerequisite for air combat situation awareness,trajectory prediction,threat assessment and maneuver decision.To get rid of the dependence of the current target maneuver recognition method on empirical criteria and sample data,and automatically and adaptively complete the task of extracting the target maneuver pattern,in this paper,an air combat maneuver pattern extraction based on time series segmentation and clustering analysis is proposed by combining autoencoder,G-G clustering algorithm and the selective ensemble clustering analysis algorithm.Firstly,the autoencoder is used to extract key features of maneuvering trajectory to remove the impacts of redundant variables and reduce the data dimension;Then,taking the time information into account,the segmentation of Maneuver characteristic time series is realized with the improved FSTS-AEGG algorithm,and a large number of maneuver primitives are extracted;Finally,the maneuver primitives are grouped into some categories by using the selective ensemble multiple time series clustering algorithm,which can prove that each class represents a maneuver action.The maneuver pattern extraction method is applied to small scale air combat trajectory and can recognize and correctly partition at least 71.3%of maneuver actions,indicating that the method is effective and satisfies the requirements for engineering accuracy.In addition,this method can provide data support for various target maneuvering recognition methods proposed in the literature,greatly reduce the workload and improve the recognition accuracy.
基金This work was supported by the Aeronautical Science Foundation of China under Grand No. 04D52032.
文摘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.
基金Supported by the National Natural Science Foundation of China(61103058,61233011)
文摘Image segmentation remains one of the major challenges in image analysis.And soft image segmentation has been widely used due to its good effect.Fuzzy clustering algorithms are very popular in soft segmentation.A new soft image segmentation method based on center-free fuzzy clustering is proposed.The center-free fuzzy clustering is the modified version of the classical fuzzy C-means ( FCM ) clustering.Different from traditional fuzzy clustering , the center-free fuzzy clustering does not need to calculate the cluster center , so it can be applied to pairwise relational data.In the proposed method , the mean-shift method is chosen for initial segmentation firstly , then the center-free clustering is used to merge regions and the final segmented images are obtained at last.Experimental results show that the proposed method is better than other image segmentation methods based on traditional clustering.
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.
文摘A novel mercer kernel based fuzzy clustering self-adaptive algorithm is presented. The mercer kernel method is introduced to the fuzzy c-means clustering. It may map implicitly the input data into the high-dimensional feature space through the nonlinear transformation. Among other fuzzy c-means and its variants, the number of clusters is first determined. A self-adaptive algorithm is proposed. The number of clusters, which is not given in advance, can be gotten automatically by a validity measure function. Finally, experiments are given to show better performance with the method of kernel based fuzzy c-means self-adaptive algorithm.
基金Science and Researching Foundation of Jiamusi University(L2006-12)
文摘This paper presents a new Section Set Adaptive FCM algorithm.The algorithm solved the shortcomings of local optimality,unsure classification and clustering numbers ascertained previously.And it improved on the architecture of FCM al- gorithm,enhanced the analysis for effective clustering.During the clustering processing,it may adjust clustering numbers dy- namically.Finally,it used the method of section set decreasing the time of classification.By experiments,the algorithm can im- prove dependability of clustering and correctness of classification.
基金supported by the National Natural Science Youth Foundation of China(No.41501283)the Fundamental Research Funds for the Central Universities(2015ZCQGX-04)
文摘The complex geometry and topology of soil is widely recognised as the key driver in many ecological processes. X-ray computed tomography (CT) provides insight into the internal structure of soil pores automatically and accurately. Until recently, there have not been methods to identify soil pore structures. This has restricted the development of soil science, particularly regarding pore geometry and spatial distribution. Through the adoption of the fuzzy clustering theory and the establishment of pore identification rules, a novel pore identification method is described to extract pore structures from CT soil images. The robustness of the adaptive fuzzy C-means method (AFCM), the adaptive threshold method, and Image-Pro Plus tools were compared on soil specimens under different conditions, such as frozen, saturated, and dry situations. The results demonstrate that the AFCM method is suitable for identifying pore clusters, especially tiny pores, under various soil conditions. The method would provide an optional technique for the study of soil micromorphology.
基金Project 20050290010 supported by the Doctoral Foundation of Chinese Education Ministry
文摘Spontaneous combustion is one of the greatest disasters in coal mines. Early recognition is important because it may be a potential inducement for other coalmine accidents. However, early recognition is difficult because of the complexity of different coal mines. Fuzzy clustering has been proposed to incorporate the uncertainty of spontaneous combustion in coal mines and it can give a clear degree of classification of combustion. Because FCM clustering tends to become trapped in local minima, a new approach of fuzzy c-means clustering based on a genetic algorithm is there- fore proposed. Genetic algorithm is capable of locating optimal or near optimal solutions to difficult problems. It can be applied in many fields without first obtaining detailed knowledge about correlation. It is helpful in improving the effec- tiveness of fuzzy clustering in detecting spontaneous combustion. The effectiveness of the method is demonstrated by means of an experiment.
基金Supported by the National Natural Science Foundation of China under Grant No.50909025/E091002the Open Research Foundation of SKLab AUV, HEU under Grant No.2008003
文摘The S/N of an underwater image is low and has a fuzzy edge.If using traditional methods to process it directly,the result is not satisfying.Though the traditional fuzzy C-means algorithm can sometimes divide the image into object and background,its time-consuming computation is often an obstacle.The mission of the vision system of an autonomous underwater vehicle (AUV) is to rapidly and exactly deal with the information about the object in a complex environment for the AUV to use the obtained result to execute the next task.So,by using the statistical characteristics of the gray image histogram,a fast and effective fuzzy C-means underwater image segmentation algorithm was presented.With the weighted histogram modifying the fuzzy membership,the above algorithm can not only cut down on a large amount of data processing and storage during the computation process compared with the traditional algorithm,so as to speed up the efficiency of the segmentation,but also improve the quality of underwater image segmentation.Finally,particle swarm optimization (PSO) described by the sine function was introduced to the algorithm mentioned above.It made up for the shortcomings that the FCM algorithm can not get the global optimal solution.Thus,on the one hand,it considers the global impact and achieves the local optimal solution,and on the other hand,further greatly increases the computing speed.Experimental results indicate that the novel algorithm can reach a better segmentation quality and the processing time of each image is reduced.They enhance efficiency and satisfy the requirements of a highly effective,real-time AUV.
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supported by the National Natural Science Foundation of China under Grant No. 30525030,60701015, and 60736029
文摘In recent years, microarray technology has been widely applied in biological and clinical studies for simultaneous monitoring of gene expression in thousands of genes. Gene clustering analysis is found useful for discovering groups of correlated genes potentially co-regulated or associated to the disease or conditions under investigation. Many clustering methods including k-means, fuzzy c-means, and hierarchical clustering have been widely used in literatures. Yet no comprehensive comparative study has been performed to evaluate the effectiveness of these methods, specially, in yeast saccharomyces cerevisiae. In this paper, these three gene clustering methods are compared. Classification accuracy and CPU time cost are employed for measuring performance of these algorithms. Our results show that hierarchical clustering outperforms k-means and fuzzy c-means clustering. The analysis provides deep insight to the complicated gene clustering problem of expression profile and serves as a practical guideline for routine microarray cluster analysis of gene expression.
基金The 15th Plan National Defence Preven-tive Research Project (No.413030201)
文摘To deal with the nonlinear separable problem, the generalized noise clustering （GNC） algorithm is extended to a kernel generalized noise clustering （KGNC） model. Different from the fuzzy c-means （FCM） model and the GNC model which are based on Euclidean distance, the presented model is based on kernel-induced distance by using kernel method. By kernel method the input data are nonlinearly and implicitly mapped into a high-dimensional feature space, where the nonlinear pattern appears linear and the GNC algorithm is performed. It is unnecessary to calculate in high-dimensional feature space because the kernel function can do it just in input space. The effectiveness of the proposed algorithm is verified by experiments on three data sets. It is concluded that the KGNC algorithm has better clustering accuracy than FCM and GNC in clustering data sets containing noisy data.
