搜索资源列表
KFCMClust
- 基于matlab的核模糊C-means聚类算法(KFCM),-Matlab-based nuclear Fuzzy C-means clustering algorithm (SN)
KFCM
- 基于粗糙熵和K-均值聚类算法的图像分割
FCM_KFCM
- fcm kfcm segmentation
FCM
- 基于matlab写的HCM和FCM程序，以及快速算法KFCM算法，对写毕业设计的很有用-Based on matlab HCM and FCM written procedures and fast algorithm for writing, KFCM algorithm of graduation design is very useful
KFCMClust529
- 标准的模糊C聚类算法程序，做聚类图像处理的非常有用-the corresponding algorithms are called kernel fuzzy c-means (KFCM) and kernel possibilistic c-means (KPCM) algorithms. And some test results are given to illustrate the advantages of the proposed algorithms over
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- kfcm，为模糊核聚类算法，用于将低维的数据映射到高维进行分类，是较先进的算法-kfcm, the fuzzy kernel clustering algorithm for low-dimensional data is mapped to high-dimensional classification, is a more advanced algorithms
kfcm
- 基于核的FCM，可以用于聚类或者图像分割，但运行速度不是很快 自己不知道如何优化-Kernel-based FCM, can be used for clustering or image segmentation, but the speed is not fast they do not know how to optimize
kfcm
- 基于核函数的模糊C聚类均值（fcm）算法-FCM algorithm based on kernel function
KFCM-nuclear-clustering-algorithm
- 核聚类算法KFCM源代码，对传统FCM算法的改进-Nuclear clustering algorithm KFCM source code, the traditional FCM Algorithm
KFCM
- 经典KFCM算法在iris数据库上的使用！-The classic KFCM algorithm in the iris database!
KFCM
- 利用核模糊C均值聚类KFCM算法实现对数据的分类-Nuclear Fuzzy C-Means Clustering KFCM algorithm on data classification
kfcm
- KFCM基于核函数的图像分割，分割为5个图片-KFCM 5
KFCM
- 核模糊C均值聚类（KFCM）算法，广泛应用于模糊聚类分析算法．-KFCM Algorithm
FCM-and-KFCM
- FCM algorithm and KFCM algorithm based on Matlab.
kernel
- 用kfcm算法实现数据的聚类和分类效果。(Kfcm algorithm is used to achieve data clustering and classification results.)
KFCM
- 基于核函数的FCM算法在医学图像分割上的应用(The application of FCM algorithm based on kernel function in medical image segmentation)
kfcm
- 实现了KFCM算法，对模糊图像进行聚类分析，效果良好(The KFCM algorithm is implemented)
KFCM
- KFCM 模糊核聚类 这只是KFCM的算法的文件 可以被程序调用的算法 如果有问题 把5.11式的那里的平方去掉（.^2）(KFCM fuzzy kernel clustering)
KFCM算法MATLAB
- 分别用kmeans、fcm、kfcm实现图像分割(use kmeans, fcm and kfcm segment image)
FCM+KFCM模糊C均值聚类分析算法
- FCM+KFCM聚类分析两种方法的比较，有聚类效果图(FCM+KFCM cluster analysis of the two methods of comparison, there is a clustering effect map.)