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FCM Data set clustering using fuzzy c-means clustering.
[CENTER, U, OBJ_FCN] = FCM(DATA, N_CLUSTER) finds N_CLUSTER number of
clusters in the data set DATA. DATA is size M-by-N, where M is the number of
data points and N is the number of coor
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FCM-S（BCFCM）的matlab实现程序 It's an implementation of the paper of M.N. Ahmed et. al. "A Modified Fuzzy C-Means Algorithm for Bias Field estimation and Segmentation of MRI Data"20002, IEEE Transactions on medical imaging.
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模糊C均值聚类，实现数据聚类算法！来源于网络，经过修改，可用于数据聚类！-Fuzzy C-Means clustering, data clustering algorithm! From the network, without modification, can be used for data clustering!
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采用模糊C均值对数据集进行聚类分析的matlab实现方法 -Using Fuzzy C-Means clustering analysis of data sets of matlab implementation
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程序用matlab实现了模糊c均值聚类问题。将大量数据分成几种类型-Program using matlab realize the fuzzy c-means clustering problem. The large amounts of data into several types of classes
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we present an improved fuzzy
C-means (FCM) algorithm for image segmentation by introducing
a tradeoff weighted fuzzy factor and a kernel metric. The
tradeoff weighted fuzzy factor depends on the space distance of
all neighboring pixels and th
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模糊C均值聚类算法过程，C语言编写，有测试数据-Fuzzy C-means clustering algorithm process, C language, test data
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采用FCM(模糊C均值)算法将数据集data聚为cluster_n类.-Using FCM (Fuzzy C-Means) algorithm dataset data clustered into cluster_n class.
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Data clustering by using Fuzzy c means, where c means number of cluster, m means weight of membership function matrix, X means data input.
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采用模糊C均值对数据集进行聚类的matlab代码-Matlab code for data clustering based on fuzzy c-means algorithm
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resonance imaging (MRI) data and estimation
of intensity inhomogeneities using fuzzy logic. MRI intensity
inhomogeneities can be attributed to imperfections in the
radio-frequency coils or to problems associated with the acquisition
sequences
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IFP_FCM,改进型FCM(模糊C均值)聚类算法,由Frank Hoppner与Frank Klawonn于2003年提出,解决了FCM算法对初始值设定较为敏感、训练速度慢、在迭代时容易陷入局部极小的问题。并附带了Box和Jenkins煤气炉数据模型辨识实例。-IFP_FCM, modified FCM (Fuzzy C-Means) clustering algorithm, in 2003, proposed by Frank Hoppner and Frank Klawonn, FCM
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使用模糊C均值算法对数据集进行聚类分析，效果良好。(The fuzzy C means algorithm is used to cluster the data sets, and the results are good.)
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Fuzzy clustering algorithms like the popular fuzzy c-means algorithm (FCM) are
frequently used to automatically divide up the data space into fuzzy granules. When the fuzzy clusters are used to derive membership functions for a fuzzy rule-based syst
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采用模糊C均值对数据集进行聚类，是一种很有效的聚类方法(Clustering data sets using fuzzy C-means is a very effective clustering method)
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该代码是对文章A Modified Fuzzy C-Means Algorithm for Bias Field Estimation and Segmentation of MRI Data算法的实现(the code of A Modified Fuzzy C-Means Algorithm for Bias Field Estimation and Segmentation of MRI Data)
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在文章A Modified Fuzzy C-Means Algorithm for Bias Field Estimation and Segmentation of MRI Data中加入了核估计算法(the modified code of A Modified Fuzzy C-Means Algorithm for Bias Field Estimation and Segmentation of MRI Data)
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FCM算法是一种基于划分的聚类算法，它的思想就是使得被划分到同一簇的对象之间相似度最大，而不同簇之间的相似度最小。模糊C均值算法是普通C均值算法的改进，普通C均值算法对于数据的划分是硬性的，而FCM则是一种柔性的模糊划分。在介绍FCM具体算法之前我们先介绍一些模糊集合的基本知识。(The FCM algorithm is a partition-based clustering algorithm. Its idea is to make the similarity among the obj
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模糊c-均值聚类算法 fuzzy c-means algorithm (FCMA)或称（ FCM）。在众多模糊聚类算法中，模糊C-均值（ FCM） 算法应用最广泛且较成功，它通过优化目标函数得到每个样本点对所有类中心的隶属度，从而决定样本点的类属以达到自动对样本数据进行分类的目的。(Fuzzy c-means clustering algorithm or FCM. Among many fuzzy clustering algorithms, the Fuzzy C-Means algori
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