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模糊c-均值算法是模糊聚类中最常见、应用最广泛的分析方法之一.经典FCM算法理论和应用的研究已经相当成熟,Bezdek业已证明算法的收敛性[6],许多软件提供了多种方便的FCM工具箱(如Matlab中FCM工具箱等).但是传统FCM算法处理的是普通数据集,不能直接处理区间数,得到的聚类原型也不是区间数.针对区间数,一种直觉的方法是分别对左区间值和右区间值作FCM,并把得到的聚类原型分别作为区间型聚类原型的左右区间值,但这种方法已经被证明是行不通的[6]-fuzzy c-means clustering algorithm is fuzzy most common use of the most extensive analysis of one of the methods. FCM classical theory and application of the research is already quite mature, PET has proved the convergence of the algorithm [6] Many software provides a variety of convenient FCM toolbox (eg Matlab toolbox FCM). But traditional FCM is the common data sets, not directly deal with the interval, The prototype is not clustering interval. Against interval, an intuitive method is to separate the left and right interval interval for the FCM, and the prototype were cluster-cluster as a prototype interval around the interval, But this method has been proven to be unworkable [6]
Update : 2008-10-13 Size : 9098 Publisher : feifei

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 - Means Algori thms (FCMA).
Update : 2008-10-13 Size : 3377 Publisher : 李兵

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UNIX下使用的基于模糊C聚类分析的原码-use of UNIX-based C Fuzzy Cluster Analysis of the original code
Update : 2024-05-19 Size : 957440 Publisher : 腾飞

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模糊聚类分析传递闭包法FCM的C语言程序, 能对数据进行分类,广泛应用于模式识别中-Fuzzy cluster analysis method FCM transitive closure of the C language program that can classify the data is widely used in Pattern Recognition
Update : 2024-05-19 Size : 1024 Publisher : 王璐瑶

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一个用VC编写的聚类案例,可以实现C聚类、模糊聚类和应用遗传算法对样品进行聚类分析 -Written in a cluster with VC cases, can be achieved C clustering, fuzzy clustering and genetic algorithm for cluster analysis of samples
Update : 2024-05-19 Size : 7714816 Publisher : 刘钢

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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 (FCM), known as fuzzy ISODATA, is used to determine membership of each data point belongs to a cluster of a clustering algorithm level.
Update : 2024-05-19 Size : 4096 Publisher : lijuan

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fuzzy cluster means nice algorithm -fuzzy cluster means nice algorithm !!
Update : 2024-05-19 Size : 65536 Publisher : yac

This work presents an implementation of a parallel Fuzzy c-means cluster analysis tool, which implements both aspects of cluster investigation: the calculation of clusters’ centers with the degrees of membership of records to clusters, and the determination of the optimal number of clusters for a given dataset using the PBM index.
Update : 2024-05-19 Size : 258048 Publisher : samira

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weka聚类算法中的基础算法,可以作为实验添加到weka中,与其他算法做对比-basic alogrithm in cluster
Update : 2024-05-19 Size : 1024 Publisher : 蓝差

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 points belong to a cluster of degree of a kind of clustering algorithm.
Update : 2024-05-19 Size : 3072 Publisher : 金丰

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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 similarity between the objects, while the minimum similarity between different clusters. Fuzzy C-Means algorithm is an ordinary C-means algorithm, normal C-means algorithm for data classification is rigid, and FCM is a flexible fuzzy partition.
Update : 2024-05-19 Size : 2112512 Publisher : 张瑞洁

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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
Update : 2024-05-19 Size : 23552 Publisher : Jane

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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++
Update : 2024-05-19 Size : 1024 Publisher : 暗暗

This code is clustering in wsn based on fuzzy logic and you can see network life time in this experiment and number of cluster head per round
Update : 2024-05-19 Size : 3072 Publisher : sped

OtherFcm
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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 coordinates for each data point. The coordinates for each cluster center are returned in the rows of the matrix CENTER. The membership function matrix U contains the grade of membership of each DATA point in each cluster. The values 0 and 1 indicate no membership and full membership respectively. Grades between 0 and 1 indicate that the data point has partial membership in a cluster. At each iteration, an objective function is minimized to find the best location for the clusters and its values are returned in OBJ_FCN.
Update : 2024-05-19 Size : 1024 Publisher : almosawi

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聚类工具箱,可以实现k-means,fuzzy c-means,agglomerative (hierarchical) clustering等聚类-cluster toolbox
Update : 2024-05-19 Size : 13312 Publisher : 李志猛

进行模糊C均值聚类,读取数据,并进行标准化变换-Fuzzy C-means clustering, data is read and converted to standardize
Update : 2024-05-19 Size : 5120 Publisher : 宋孝洋

Partition index is a measure ofvalidity similar to partition coeGcient, based on using Pj = ci=1 (uij)m as a measure ofhow well the jth data point has been classi- 2ed. The closer a pixel is to a codebook entry, the closer Pj is to one. Ifa pixel becomes an outlier to all cluster centers, the value of Pj approaches 1=cm − 1, which is the minimum value it can have.
Update : 2024-05-19 Size : 60416 Publisher : dr

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
Update : 2024-05-19 Size : 123904 Publisher : kikiren

matlabFCM
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使用模糊C均值聚类(FCM)的方法对状态进行分类,其优点首先是可以根据实际情况自动确定聚类中心,减少人工干涉的因素,其次,对状态特征参数不是进行硬分类,而是通过隶属度的表征方式对其聚类,更加符合现实状态类别之间不具备明显界限的实际问题。(The use of fuzzy C mean clustering (FCM) method to classify the state, its advantage is first can automatically determine the cluster center according to the actual situation, to reduce the interference of artificial factors, secondly, the state parameters are not hard classification, but through the characterization of Party membership of the cluster, more practical problems have not obvious the boundaries between reality state categories.)
Update : 2024-05-19 Size : 7168 Publisher : 蒙奇H
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