fuzzyc-means (FcM) algorithm is a fuzzy pattern recognition method. clustering precision of the algorithm is affectedby its equal partition trend for data set of large discrepancy of each class samples number, and th...
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ISBN:
(纸本)9780819469526
fuzzyc-means (FcM) algorithm is a fuzzy pattern recognition method. clustering precision of the algorithm is affected
by its equal partition trend for data set of large discrepancy of each class samples number, and the optimal clustering
result of the algorithm mightn't be a right partition in this case. In order to overcome this disadvantage, a Gaussian
function weightedfuzzyc-means (WFcM) algorithm is proposed, which the weighted function is produced by a
Gaussian function calculating dot density of each sample. To certain extent, the WFcM algorithm has not only overcome
the limitation of equal partition trend in fuzzycmeansalgorithm, but also been favorable convergence and stability. The
calculation of the weighted function and the choice of sample dot density range restriction value for the algorithm are
both objective. When partially supervised information obtained from a few labeled samples is introduced to the WFcM
algorithm, the classification performance of the WFcM algorithm is further enhanced and the convergent speed of
objective function is further accelerated.
In this study, a revised weighted fuzzy c-means algorithm is proposed for uncapacitated planar multi-facility location problems. It eliminates the obligation to sequentially use different methods such as classical fuz...
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In this study, a revised weighted fuzzy c-means algorithm is proposed for uncapacitated planar multi-facility location problems. It eliminates the obligation to sequentially use different methods such as classical fuzzyc-meansalgorithm, combination of fuzzyc-means and center of gravity, and particle swarm optimization algorithm. Performance of the proposed algorithm for uncapacitated planar multi-facility location problem is tested on well-known research data sets. This new algorithm is compared with the methods including fuzzyc-means, fuzzyc-means based center of gravity and particle swarm optimization. Results indicate that the proposed revised weighted fuzzy c-means algorithm based method is superior in terms of cost minimization and cPU time.
The hardness prediction model was established by support vector regression(SVR).In order to avoid exaggerating the contribution of very tiny alloying elements,a weightedfuzzyc-means(WFcM)algorithm was proposed for d...
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The hardness prediction model was established by support vector regression(SVR).In order to avoid exaggerating the contribution of very tiny alloying elements,a weightedfuzzyc-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 *** samples were divided into two *** top 10 features of each class were selected to form two feature subsets for better performance of the *** dimension and dispersion of features decreased in such feature *** four machine learning algorithms,SVR had the best performance and was chosen to *** hyper-parameters of the SVR model were optimized by particle swarm *** 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 *** 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 *** indicated that WFcM was an effective method to reduce the dispersion of features and improve the accuracy of model.
Based on the uncertainty and fuzziness of remote sensing images, a dot density function weightedfuzzyc-means (WFcM) clustering algorithm is proposed to carry out the fuzzyclassification or the hard classification o...
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ISBN:
(纸本)9781424412112
Based on the uncertainty and fuzziness of remote sensing images, a dot density function weightedfuzzyc-means (WFcM) clustering algorithm is proposed to carry out the fuzzyclassification or the hard classification of remote sensing images. First, the algorithmconsidering data spatial distributing information and classification fuzziness is described. fuzzyc-meansalgorithm is an unsupervised fuzzyclassification method. clustering precision of the algorithm is affected by its equal partition trend for data sets, which leads that the optimal solution of the algorithm may not be the correct partition in the data set of which cluster sample numbers are difference greatly. In order to overcome this drawback, a dot density function WFcM algorithm is proposed in this paper. The method has not only overcome the limitation of FcM to certain extent, but also been favorable convergence. Then the WFcM algorithm would be compared with the K-meansalgorithms by experiments in LANDSAT TM image. Finally classification result of the algorithms is analyzed systematically, and the experiment result shows the WFcM algorithmcan improve classification accuracy for remote sensing images.
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