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In fuzzy cluster analysis, many algorithms have been developed. In general, the most widely used is the Fuzzy c-Means Algorithms (FCMA).-fuzzy cluster analysis, many algorithms have been developed. In genera l the most widely used is the fuzzy c - Me
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K-means Fuzzy Clustering file
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code for fuzzy k-mean clustering-code for fuzzy k-mean clustering..
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模糊C均值聚类算法的步骤还是比较简单的,模糊C均值聚类(FCM),即众所周知的模糊ISODATA,是用隶属度确定每个数据点属于某个聚类的程度的一种聚类算法。-Fuzzy c-means clustering algorithm steps it is quite simple, fuzzy c-means clustering (FCM), known as fuzzy membership ISODATA, it is to use to make sure that each data po
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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。-FCM algorithm is a clustering algorithm based on division, the idea is to make it to the same cluster is divided into the biggest similari
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matlab开发的FCM(模糊c-均值聚类算法)例程-matlab,cluster,fuzzy c-means,FCM,code
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用类Cluster实现一维数据的模糊C均值聚类,可在VC++和BC++下使用-Class Cluster fuzzy C-means clustering, one-dimensional data can be used in VC++ and BC++
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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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模糊C均值聚类(FCM)算法是一种经典的模糊聚类分析方法,但其算法初始聚类中心集是随机选取的,从而造成算法的性能强烈的依赖聚类中心集的初始化。-Fuzzy C-Means clustering (FCM) algorithm is a classical fuzzy clustering analysis method, but the algorithm sets the initial cluster centers are selected randomly, resulting in a
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在众多聚类算法中,基于划分的模糊聚类算法是模式识别中最常用的算法类型之一.至今,文献中仍不断
有关于基于划分的模糊聚类算法的研究成果出现.为了能更为系统和深入地了解这些聚类算法及其性质,本文从改
变度量方式、改变约束条件、在目标函数中引入熵以及考虑对聚类中心进行约束等几个方面,对在 C-均值算法的
基础上得到的基于划分的模糊聚类算法作了综述和评价,对各典型算法的优缺点进行了实验比较分析.指出标准
FCM算法被广泛应用的原因之一是它对数据的比例变化具有鲁棒性,而其他类似的算法对这种
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模糊C-均值算法容易收敛于局部极小点,为了克服该缺点,将遗传算法应用于模糊C-均值算法(FCM)的优化计算中,由遗传算法得到初始聚类中心,再使用标准的模糊C-均值聚类算法得到最终的分类结果。-Fuzzy C- means algorithm is easy to converge to a local minimum point, in order to overcome this drawback, the genetic algorithm is applied to fuzzy C- me
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这是模糊C均值聚类求出聚类中心以后,待检测样本与聚类中心的贴近程度的计算程序-This is the fuzzy C-means clustering obtained after the cluster center, close to the level of the sample to be detected and clustering center calculation program
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核聚类算法:聚类是将一组给定的未知类标号的样本分成内在的多个类别,使得同一类中
的样本具有较高的相似度,而不同类中的样本差别大。侧重于软聚类(模糊C-均值——FCM),但其描述手段同样适合于硬聚
类(HCM)等同类问题。-Clustering algorithm: cluster is a group of unknown samples given class label into internal multiple categories, so that the same class
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这是遗传算法改进的模糊C-均值聚类MATLAB源代码,由遗传算法得到初始聚类中心,再使用标准的模糊C-均值聚类算法得到最终的分类结果。-This is the MATLAB source code of the improved fuzzy c-means clustering(FCM) based on the genetic algorithm(GA).The initial cluster centers are otained through GA,and the final clust
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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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java FCM program
it contain classes of clustering with fuzzy c means algorithm in java
-java FCM program
it contain classes of clustering with fuzzy c means algorithm in java
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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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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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一种快速的抗噪声模糊C均值图像分割算法
图像分割就是把图像分成若干个特定的、具有独特性质的区域并提出。该算法结合像素灰度值相似度和隶属度构造了一个新的空间函数。该空间函数用于更新成员关系,而成员关系又用于迭代地获取聚类中心。所提出的算法可以在较少的迭代次数下获得理想的分割结果,有效地降低了噪声的影响。(A fast anti noise Fuzzy C-Means Image Segmentation AlgorithmImage segmentation is to divide the i
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